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train_n_evalauate.py
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train_n_evalauate.py
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import os
import time
from collections import deque
import numpy as np
import torch
from algorithm import algo, utils
from algorithm.algo import gail
from algorithm.arguments import get_args
from algorithm.envs import make_vec_envs
from algorithm.model import Policy
from algorithm.storage import RolloutStorage
from algorithm.evaluation import evaluate
from stable_baselines3.common.running_mean_std import RunningMeanStd
def learn_the_2_sampled_envs(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
env_name1 = args.expert_path1.split("_")[1]
env_name2 = args.expert_path2.split("_")[1]
env_par1 = env_name1.split("-")[0][-4:]
env_par2 = env_name2.split("-")[0][-4:]
assert env_name1.split("-")[0][:2] == args.env_name[:2]
assert env_name2.split("-")[0][:2] == args.env_name[:2]
excute_name = "2_sampled_envs_" + str(args.algo_name) + "_" + str(env_par1) + str(env_par2) + "_" \
+ str(args.env_name) + "_" + str(args.env_parameter) + "_" + str(args.seed) + "seed"
if args.log_dir != None:
log_dir = os.path.expanduser(args.log_dir) + excute_name
eval_log_dir = log_dir + "_eval"
utils.cleanup_log_dir(log_dir + "_env1")
utils.cleanup_log_dir(log_dir + "_env2")
torch.set_num_threads(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
envs1 = make_vec_envs(env_name1, args.seed, args.num_processes, args.gamma, log_dir + "_env1", device, False, use_rms=False)
envs2 = make_vec_envs(env_name2, args.seed, args.num_processes, args.gamma, log_dir + "_env2", device, False, use_rms=False)
if not args.no_rms:
total_obs_rms = RunningMeanStd(shape=envs1.observation_space.shape)
def total_obsfilt(obs, update=False):
return np.clip((obs - total_obs_rms.mean) / np.sqrt(total_obs_rms.var + 1e-8), -10.0, 10.0)
actor_critic = Policy(envs1.observation_space.shape, envs1.action_space,
base_kwargs={'recurrent': args.recurrent_policy})
actor_critic.to(device)
agent = algo.PPO(actor_critic, args.clip_param, args.ppo_epoch, args.num_mini_batch, args.value_loss_coef,
lr=args.lr, eps=args.eps, max_grad_norm=args.max_grad_norm)
assert len(envs1.observation_space.shape) == 1
assert len(envs2.observation_space.shape) == 1
if "rime+wsd" in args.algo_name.lower():
discr1 = gail.WeightShared_discriminator(2, envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr2 = gail.WeightShared_discriminator(2, envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
elif (("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower())) or "omme" in args.algo_name.lower():
discr11 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr12 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr21 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr22 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
elif "mixture" in args.algo_name.lower():
discr = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
elif "single" in args.algo_name.lower():
discr1 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr2 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
else:
print("Check algo-name")
import pdb; pdb.set_trace()
file_name1 = "./expert_demonstrations/" + args.expert_path1 + ".pt"
file_name2 = "./expert_demonstrations/" + args.expert_path2 + ".pt"
expert_dataset1 = gail.ExpertDataset(file_name1, num_trajectories=0, subsample_frequency=1)
expert_dataset2 = gail.ExpertDataset(file_name2, num_trajectories=0, subsample_frequency=1)
drop_last1 = len(expert_dataset1) > args.gail_batch_size
drop_last2 = len(expert_dataset2) > args.gail_batch_size
gail_train_loader1 = torch.utils.data.DataLoader(dataset=expert_dataset1, batch_size=args.gail_batch_size, shuffle=True, drop_last=drop_last1)
gail_train_loader2 = torch.utils.data.DataLoader(dataset=expert_dataset2, batch_size=args.gail_batch_size, shuffle=True, drop_last=drop_last2)
rollouts1 = RolloutStorage(args.num_steps, args.num_processes, envs1.observation_space.shape, envs1.action_space, actor_critic.recurrent_hidden_state_size)
rollouts2 = RolloutStorage(args.num_steps, args.num_processes, envs2.observation_space.shape, envs2.action_space, actor_critic.recurrent_hidden_state_size)
obs1 = envs1.reset()
obs2 = envs2.reset()
if not args.no_rms:
total_obs_rms.update(obs1)
total_obs_rms.update(obs2)
obs1 = torch.from_numpy(obs1).float().to(device)
obs2 = torch.from_numpy(obs2).float().to(device)
rollouts1.obs[0].copy_(obs1)
rollouts1.to(device)
rollouts2.obs[0].copy_(obs2)
rollouts2.to(device)
episode_rewards1 = deque(maxlen=20)
episode_rewards2 = deque(maxlen=20)
num_updates = int(args.num_env_steps) // args.num_steps // args.num_processes
for j in range(num_updates):
if args.use_linear_lr_decay:
utils.update_linear_schedule(agent.optimizer, j, num_updates, args.lr)
for step in range(args.num_steps):
# Sample actions
with torch.no_grad():
value1, action1, action_log_prob1, recurrent_hidden_states1 = actor_critic.act(rollouts1.obs[step],
rollouts1.recurrent_hidden_states[step],
rollouts1.masks[step])
value2, action2, action_log_prob2, recurrent_hidden_states2 = actor_critic.act(rollouts2.obs[step],
rollouts2.recurrent_hidden_states[step],
rollouts2.masks[step])
# Obser reward and next obs
obs1, _, done1, infos1 = envs1.step(action1)
obs2, _, done2, infos2 = envs2.step(action2)
if not args.no_rms:
temp_obs1 = obs1
temp_obs2 = obs2
obs1 = total_obsfilt(obs1)
obs2 = total_obsfilt(obs2)
if j < args.rms_iters + 1:
total_obs_rms.update(temp_obs1)
total_obs_rms.update(temp_obs2)
obs1 = torch.from_numpy(obs1).float().to(device)
obs2 = torch.from_numpy(obs2).float().to(device)
for info1 in infos1:
if 'episode' in info1.keys():
episode_rewards1.append(info1['episode']['r'])
for info2 in infos2:
if 'episode' in info2.keys():
episode_rewards2.append(info2['episode']['r'])
# If done then clean the history of observations.
masks1 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done1])
masks2 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done2])
bad_masks1 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos1])
bad_masks2 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos2])
rollouts1.insert(obs1, recurrent_hidden_states1, action1, action_log_prob1, value1, torch.zeros(1, 1), masks1, bad_masks1)
rollouts2.insert(obs2, recurrent_hidden_states2, action2, action_log_prob2, value2, torch.zeros(1, 1), masks2, bad_masks2)
with torch.no_grad():
next_value1 = actor_critic.get_value(rollouts1.obs[-1], rollouts1.recurrent_hidden_states[-1], rollouts1.masks[-1]).detach()
next_value2 = actor_critic.get_value(rollouts2.obs[-1], rollouts2.recurrent_hidden_states[-1], rollouts2.masks[-1]).detach()
if j >= args.rms_iters:
envs1.venv.eval()
envs2.venv.eval()
gail_epoch = args.gail_epoch
if j < args.rms_iters:
gail_epoch = args.initial_dstep # Warm up
for _ in range(gail_epoch):
if "rime+wsd" in args.algo_name.lower():
discr1.update_WSD_2env(rollouts1, gail_train_loader1, gail_train_loader2, rms=total_obs_rms)
discr2.update_WSD_2env(rollouts2, gail_train_loader1, gail_train_loader2, rms=total_obs_rms)
elif (("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower())) or "omme" in args.algo_name.lower():
discr11.update_gp(gail_train_loader1, rollouts1, total_obs_rms)
discr12.update_gp(gail_train_loader2, rollouts1, total_obs_rms)
discr21.update_gp(gail_train_loader1, rollouts2, total_obs_rms)
discr22.update_gp(gail_train_loader2, rollouts2, total_obs_rms)
elif "mixture" in args.algo_name.lower():
discr.update_gp_mixture_2envs(gail_train_loader1, gail_train_loader2, rollouts1, rollouts2, total_obs_rms)
elif "single" in args.algo_name.lower():
discr1.update_gp(gail_train_loader1, rollouts1, total_obs_rms)
discr2.update_gp(gail_train_loader2, rollouts2, total_obs_rms)
roll_temp_1 = [[], []]
roll_temp_2 = [[], []]
for step in range(args.num_steps):
if "rime+wsd" in args.algo_name.lower():
rollouts1.rewards[step] = torch.min(discr1.predict_reward(0, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr1.predict_reward(1, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
rollouts2.rewards[step] = torch.min(discr2.predict_reward(0, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr2.predict_reward(1, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
elif ("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower()):
rollouts1.rewards[step] = torch.min(discr11.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr12.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
rollouts2.rewards[step] = torch.min(discr21.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr22.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
elif "omme" in args.algo_name.lower():
roll_temp_1[0].append(discr11.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_1[1].append(discr12.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_2[0].append(discr21.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_2[1].append(discr22.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
elif "mixture" in args.algo_name.lower():
rollouts1.rewards[step] = discr.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])
rollouts2.rewards[step] = discr.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])
elif "single" in args.algo_name.lower():
rollouts1.rewards[step] = discr1.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])
rollouts2.rewards[step] = discr2.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])
if "omme" in args.algo_name.lower():
index_roll1 = np.argmin([torch.mean(torch.tensor(roll_temp_1[0])), torch.mean(torch.tensor(roll_temp_1[1]))])
index_roll2 = np.argmin([torch.mean(torch.tensor(roll_temp_2[0])), torch.mean(torch.tensor(roll_temp_2[1]))])
for step in range(args.num_steps):
rollouts1.rewards[step] = roll_temp_1[index_roll1][step]
rollouts2.rewards[step] = roll_temp_2[index_roll2][step]
rollouts1.compute_returns(next_value1, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
rollouts2.compute_returns(next_value2, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
_, _, _ = agent.update_2envs(rollouts1, rollouts2)
rollouts1.after_update()
rollouts2.after_update()
if len(episode_rewards1) > 1 and len(episode_rewards2) > 1:
total_num_steps = (j + 1) * args.num_processes * args.num_steps
print("Updates {}, num timesteps {} \n Last {} training episodes \n mean/median reward1 {:.1f}/{:.1f}, reward2 {:.1f}/{:.1f} \n min/max reward1 {:.1f}/{:.1f}, reward2 {:.1f}/{:.1f}\n"
.format(j, total_num_steps, len(episode_rewards1),
np.mean(episode_rewards1), np.median(episode_rewards1),
np.mean(episode_rewards2), np.median(episode_rewards2),
np.min(episode_rewards1), np.max(episode_rewards1),
np.min(episode_rewards2), np.max(episode_rewards2)))
if (len(episode_rewards1) > 1) and (len(episode_rewards2) > 1):
perfs = []
env_list = []
env_par_list_1par = ['010', '015', '020', '025', '030', '035', '040', '045', '050', '055', '060', '065', '070',
'075', '080', '085', '090', '095', 'nominal', '105', '110', '115', '120', '125', '130',
'135', '140', '145', '150', '155', '160', '165', '170', '175', '180', '185', '190', '195',
'200', '205', '210', '215', '220', '225', '230']
env_n = args.env_name.split("-")
for par in env_par_list_1par:
if par == 'nominal':
env_list.append(env_n[0] + '-' + env_n[1])
else:
if args.env_parameter == 'gravity':
env_list.append(env_n[0] + par + 'g-' + env_n[1])
elif args.env_parameter == 'mass':
env_list.append(env_n[0] + par + 'm-' + env_n[1])
for env_name in env_list:
perfs.append(evaluate(actor_critic, total_obs_rms, env_name, args.seed, args.num_processes, eval_log_dir, device))
return [perfs, excute_name]
def learn_the_3_sampled_envs(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
env_name1 = args.expert_path1.split("_")[1]
env_name2 = args.expert_path2.split("_")[1]
env_name3 = args.expert_path3.split("_")[1]
env_par1 = env_name1.split("-")[0][-4:]
env_par2 = env_name2.split("-")[0][-4:]
env_par3 = env_name3.split("-")[0][-4:]
assert env_name1.split("-")[0][:2] == args.env_name[:2]
assert env_name2.split("-")[0][:2] == args.env_name[:2]
assert env_name3.split("-")[0][:2] == args.env_name[:2]
excute_name = "3_sampled_envs_" + str(args.algo_name) + "_" + str(env_par1) + str(env_par2) + str(env_par3) + "_" \
+ str(args.env_name) + "_" + str(args.env_parameter) + "_" + str(args.seed) + "seed"
if args.log_dir != None:
log_dir = os.path.expanduser(args.log_dir) + excute_name
eval_log_dir = log_dir + "_eval"
utils.cleanup_log_dir(log_dir + "_env1")
utils.cleanup_log_dir(log_dir + "_env2")
utils.cleanup_log_dir(log_dir + "_env3")
torch.set_num_threads(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
envs1 = make_vec_envs(env_name1, args.seed, args.num_processes, args.gamma, log_dir + "_env1", device, False, use_rms=False)
envs2 = make_vec_envs(env_name2, args.seed, args.num_processes, args.gamma, log_dir + "_env2", device, False, use_rms=False)
envs3 = make_vec_envs(env_name3, args.seed, args.num_processes, args.gamma, log_dir + "_env3", device, False, use_rms=False)
if not args.no_rms:
total_obs_rms = RunningMeanStd(shape=envs1.observation_space.shape)
def total_obsfilt(obs, update=False):
return np.clip((obs - total_obs_rms.mean) / np.sqrt(total_obs_rms.var + 1e-8), -10.0, 10.0)
actor_critic = Policy(envs1.observation_space.shape,envs1.action_space,
base_kwargs={'recurrent': args.recurrent_policy})
actor_critic.to(device)
agent = algo.PPO(actor_critic, args.clip_param, args.ppo_epoch, args.num_mini_batch, args.value_loss_coef,
lr=args.lr, eps=args.eps, max_grad_norm=args.max_grad_norm)
assert len(envs1.observation_space.shape) == 1
assert len(envs2.observation_space.shape) == 1
assert len(envs3.observation_space.shape) == 1
if "rime+wsd" in args.algo_name.lower():
discr1 = gail.WeightShared_discriminator(3, envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr2 = gail.WeightShared_discriminator(3, envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr3 = gail.WeightShared_discriminator(3, envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
elif (("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower())) or "omme" in args.algo_name.lower():
discr11 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr12 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr13 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr21 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr22 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr23 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr31 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr32 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr33 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
elif "mixture" in args.algo_name.lower():
discr = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
elif "single" in args.algo_name.lower():
discr1 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr2 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr3 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
else:
print("Check algo-name")
import pdb; pdb.set_trace()
file_name1 = "./expert_demonstrations/" + args.expert_path1 + ".pt"
file_name2 = "./expert_demonstrations/" + args.expert_path2 + ".pt"
file_name3 = "./expert_demonstrations/" + args.expert_path3 + ".pt"
expert_dataset1 = gail.ExpertDataset(file_name1, num_trajectories=0, subsample_frequency=1)
expert_dataset2 = gail.ExpertDataset(file_name2, num_trajectories=0, subsample_frequency=1)
expert_dataset3 = gail.ExpertDataset(file_name3, num_trajectories=0, subsample_frequency=1)
drop_last1 = len(expert_dataset1) > args.gail_batch_size
drop_last2 = len(expert_dataset2) > args.gail_batch_size
drop_last3 = len(expert_dataset3) > args.gail_batch_size
gail_train_loader1 = torch.utils.data.DataLoader(dataset=expert_dataset1, batch_size=args.gail_batch_size,
shuffle=True, drop_last=drop_last1)
gail_train_loader2 = torch.utils.data.DataLoader(dataset=expert_dataset2, batch_size=args.gail_batch_size,
shuffle=True, drop_last=drop_last2)
gail_train_loader3 = torch.utils.data.DataLoader(dataset=expert_dataset3, batch_size=args.gail_batch_size,
shuffle=True, drop_last=drop_last3)
rollouts1 = RolloutStorage(args.num_steps, args.num_processes, envs1.observation_space.shape, envs1.action_space, actor_critic.recurrent_hidden_state_size)
rollouts2 = RolloutStorage(args.num_steps, args.num_processes, envs2.observation_space.shape, envs2.action_space, actor_critic.recurrent_hidden_state_size)
rollouts3 = RolloutStorage(args.num_steps, args.num_processes, envs3.observation_space.shape, envs3.action_space, actor_critic.recurrent_hidden_state_size)
obs1 = envs1.reset()
obs2 = envs2.reset()
obs3 = envs3.reset()
if not args.no_rms:
total_obs_rms.update(obs1)
total_obs_rms.update(obs2)
total_obs_rms.update(obs3)
obs1 = torch.from_numpy(obs1).float().to(device)
obs2 = torch.from_numpy(obs2).float().to(device)
obs3 = torch.from_numpy(obs3).float().to(device)
rollouts1.obs[0].copy_(obs1)
rollouts1.to(device)
rollouts2.obs[0].copy_(obs2)
rollouts2.to(device)
rollouts3.obs[0].copy_(obs3)
rollouts3.to(device)
episode_rewards1 = deque(maxlen=20)
episode_rewards2 = deque(maxlen=20)
episode_rewards3 = deque(maxlen=20)
num_updates = int(args.num_env_steps) // args.num_steps // args.num_processes
for j in range(num_updates):
if args.use_linear_lr_decay:
utils.update_linear_schedule(agent.optimizer, j, num_updates, args.lr)
for step in range(args.num_steps):
# Sample actions
with torch.no_grad():
value1, action1, action_log_prob1, recurrent_hidden_states1 = actor_critic.act(rollouts1.obs[step],
rollouts1.recurrent_hidden_states[step],
rollouts1.masks[step])
value2, action2, action_log_prob2, recurrent_hidden_states2 = actor_critic.act(rollouts2.obs[step],
rollouts2.recurrent_hidden_states[step],
rollouts2.masks[step])
value3, action3, action_log_prob3, recurrent_hidden_states3 = actor_critic.act(rollouts3.obs[step],
rollouts3.recurrent_hidden_states[step],
rollouts3.masks[step])
# Obser reward and next obs
obs1, _, done1, infos1 = envs1.step(action1)
obs2, _, done2, infos2 = envs2.step(action2)
obs3, _, done3, infos3 = envs3.step(action3)
if not args.no_rms:
temp_obs1 = obs1
temp_obs2 = obs2
temp_obs3 = obs3
obs1 = total_obsfilt(obs1)
obs2 = total_obsfilt(obs2)
obs3 = total_obsfilt(obs3)
if j < args.rms_iters + 1:
total_obs_rms.update(temp_obs1)
total_obs_rms.update(temp_obs2)
total_obs_rms.update(temp_obs3)
obs1 = torch.from_numpy(obs1).float().to(device)
obs2 = torch.from_numpy(obs2).float().to(device)
obs3 = torch.from_numpy(obs3).float().to(device)
for info1 in infos1:
if 'episode' in info1.keys():
episode_rewards1.append(info1['episode']['r'])
for info2 in infos2:
if 'episode' in info2.keys():
episode_rewards2.append(info2['episode']['r'])
for info3 in infos3:
if 'episode' in info3.keys():
episode_rewards3.append(info3['episode']['r'])
# If done then clean the history of observations.
masks1 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done1])
masks2 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done2])
masks3 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done3])
bad_masks1 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos1])
bad_masks2 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos2])
bad_masks3 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos3])
rollouts1.insert(obs1, recurrent_hidden_states1, action1, action_log_prob1, value1, torch.zeros(1, 1), masks1, bad_masks1)
rollouts2.insert(obs2, recurrent_hidden_states2, action2, action_log_prob2, value2, torch.zeros(1, 1), masks2, bad_masks2)
rollouts3.insert(obs3, recurrent_hidden_states3, action3, action_log_prob3, value3, torch.zeros(1, 1), masks3, bad_masks3)
with torch.no_grad():
next_value1 = actor_critic.get_value(rollouts1.obs[-1], rollouts1.recurrent_hidden_states[-1], rollouts1.masks[-1]).detach()
next_value2 = actor_critic.get_value(rollouts2.obs[-1], rollouts2.recurrent_hidden_states[-1], rollouts2.masks[-1]).detach()
next_value3 = actor_critic.get_value(rollouts3.obs[-1], rollouts3.recurrent_hidden_states[-1], rollouts3.masks[-1]).detach()
if j >= args.rms_iters:
envs1.venv.eval()
envs2.venv.eval()
envs3.venv.eval()
gail_epoch = args.gail_epoch
if j < args.rms_iters:
gail_epoch = args.initial_dstep # Warm up
for _ in range(gail_epoch):
if "rime+wsd" in args.algo_name.lower():
discr1.update_WSD_3env(rollouts1, gail_train_loader1, gail_train_loader2, gail_train_loader3, rms=total_obs_rms)
discr2.update_WSD_3env(rollouts2, gail_train_loader1, gail_train_loader2, gail_train_loader3, rms=total_obs_rms)
discr3.update_WSD_3env(rollouts3, gail_train_loader1, gail_train_loader2, gail_train_loader3, rms=total_obs_rms)
elif (("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower())) or "omme" in args.algo_name.lower():
discr11.update_gp(gail_train_loader1, rollouts1, total_obs_rms)
discr12.update_gp(gail_train_loader2, rollouts1, total_obs_rms)
discr13.update_gp(gail_train_loader3, rollouts1, total_obs_rms)
discr21.update_gp(gail_train_loader1, rollouts2, total_obs_rms)
discr22.update_gp(gail_train_loader2, rollouts2, total_obs_rms)
discr23.update_gp(gail_train_loader3, rollouts2, total_obs_rms)
discr31.update_gp(gail_train_loader1, rollouts3, total_obs_rms)
discr32.update_gp(gail_train_loader2, rollouts3, total_obs_rms)
discr33.update_gp(gail_train_loader3, rollouts3, total_obs_rms)
elif "mixture" in args.algo_name.lower():
discr.update_gp_mixture_3envs(gail_train_loader1, gail_train_loader2, gail_train_loader3, rollouts1, rollouts2, rollouts3, total_obs_rms)
elif "single" in args.algo_name.lower():
discr1.update_gp(gail_train_loader1, rollouts1, total_obs_rms)
discr2.update_gp(gail_train_loader2, rollouts2, total_obs_rms)
discr3.update_gp(gail_train_loader3, rollouts3, total_obs_rms)
roll_temp_1 = [[],[],[]]
roll_temp_2 = [[],[],[]]
roll_temp_3 = [[],[],[]]
for step in range(args.num_steps):
if "rime+wsd" in args.algo_name.lower():
rollouts1.rewards[step] = torch.min(torch.cat([discr1.predict_reward(0, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr1.predict_reward(1, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr1.predict_reward(2, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])]))
rollouts2.rewards[step] = torch.min(torch.cat([discr2.predict_reward(0, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr2.predict_reward(1, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr2.predict_reward(2, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])]))
rollouts3.rewards[step] = torch.min(torch.cat([discr3.predict_reward(0, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr3.predict_reward(1, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr3.predict_reward(2, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])]))
elif ("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower()):
rollouts1.rewards[step] = torch.min(torch.cat([discr11.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr12.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr13.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])]))
rollouts2.rewards[step] = torch.min(torch.cat([discr21.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr22.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr23.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])]))
rollouts3.rewards[step] = torch.min(torch.cat([discr31.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr32.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr33.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])]))
elif "omme" in args.algo_name.lower():
roll_temp_1[0].append(discr11.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_1[1].append(discr12.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_1[2].append(discr13.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_2[0].append(discr21.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_2[1].append(discr22.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_2[2].append(discr23.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_3[0].append(discr31.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
roll_temp_3[1].append(discr32.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
roll_temp_3[2].append(discr33.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
elif "mixture" in args.algo_name.lower():
rollouts1.rewards[step] = discr.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])
rollouts2.rewards[step] = discr.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])
rollouts3.rewards[step] = discr.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])
elif "single" in args.algo_name.lower():
rollouts1.rewards[step] = discr1.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])
rollouts2.rewards[step] = discr2.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])
rollouts3.rewards[step] = discr3.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])
if "omme" in args.algo_name.lower():
index_roll1 = np.argmin([torch.mean(torch.tensor(roll_temp_1[0])), torch.mean(torch.tensor(roll_temp_1[1])), torch.mean(torch.tensor(roll_temp_1[2]))])
index_roll2 = np.argmin([torch.mean(torch.tensor(roll_temp_2[0])), torch.mean(torch.tensor(roll_temp_2[1])), torch.mean(torch.tensor(roll_temp_2[2]))])
index_roll3 = np.argmin([torch.mean(torch.tensor(roll_temp_3[0])), torch.mean(torch.tensor(roll_temp_3[1])), torch.mean(torch.tensor(roll_temp_3[2]))])
for step in range(args.num_steps):
rollouts1.rewards[step] = roll_temp_1[index_roll1][step]
rollouts2.rewards[step] = roll_temp_2[index_roll2][step]
rollouts3.rewards[step] = roll_temp_3[index_roll3][step]
rollouts1.compute_returns(next_value1, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
rollouts2.compute_returns(next_value2, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
rollouts3.compute_returns(next_value3, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
_, _, _ = agent.update_3envs(rollouts1, rollouts2, rollouts3)
rollouts1.after_update()
rollouts2.after_update()
rollouts3.after_update()
if len(episode_rewards1) > 1 and len(episode_rewards2) > 1 and len(episode_rewards3) > 1:
total_num_steps = (j + 1) * args.num_processes * args.num_steps
print("Updates {}, num timesteps {} \n Last {} training episodes \n mean/median reward1 {:.1f}/{:.1f}, reward2 {:.1f}/{:.1f}, reward3 {:.1f}/{:.1f} \n min/max reward1 {:.1f}/{:.1f}, reward2 {:.1f}/{:.1f}, reward3 {:.1f}/{:.1f}\n"
.format(j, total_num_steps, len(episode_rewards1),
np.mean(episode_rewards1), np.median(episode_rewards1),
np.mean(episode_rewards2), np.median(episode_rewards2),
np.mean(episode_rewards3), np.median(episode_rewards3),
np.min(episode_rewards1), np.max(episode_rewards1),
np.min(episode_rewards2), np.max(episode_rewards2),
np.min(episode_rewards3), np.max(episode_rewards3)))
if (len(episode_rewards1) > 1) and (len(episode_rewards2) > 1) and (len(episode_rewards3) > 1):
perfs = []
env_list = []
env_par_list_1par = ['010', '015', '020', '025', '030', '035', '040', '045', '050', '055', '060', '065', '070',
'075', '080', '085', '090', '095', 'nominal', '105', '110', '115', '120', '125', '130',
'135', '140', '145', '150', '155', '160', '165', '170', '175', '180', '185', '190', '195',
'200', '205', '210', '215', '220', '225', '230']
env_n = args.env_name.split("-")
for par in env_par_list_1par:
if par == 'nominal':
env_list.append(env_n[0] + '-' + env_n[1])
else:
if args.env_parameter == 'gravity':
env_list.append(env_n[0] + par + 'g-' + env_n[1])
elif args.env_parameter == 'mass':
env_list.append(env_n[0] + par + 'm-' + env_n[1])
for env_name in env_list:
perfs.append(evaluate(actor_critic, total_obs_rms, env_name, args.seed, args.num_processes, eval_log_dir, device))
return [perfs, excute_name]
def learn_the_4_sampled_envs(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
env_name1 = args.expert_path1.split("_")[1]
env_name2 = args.expert_path2.split("_")[1]
env_name3 = args.expert_path3.split("_")[1]
env_name4 = args.expert_path4.split("_")[1]
env_par1 = env_name1.split("-")[0][-8:]
env_par2 = env_name2.split("-")[0][-8:]
env_par3 = env_name3.split("-")[0][-8:]
env_par4 = env_name4.split("-")[0][-8:]
assert env_name1.split("-")[0][:2] == args.env_name[:2]
assert env_name2.split("-")[0][:2] == args.env_name[:2]
assert env_name3.split("-")[0][:2] == args.env_name[:2]
assert env_name4.split("-")[0][:2] == args.env_name[:2]
excute_name = "4_sampled_envs_" + str(args.algo_name) + "_" + str(env_par1) + str(env_par2) \
+ str(env_par3) + str(env_par4) + "_" + str(args.env_name) + "_" + str(args.seed) + "seed"
if args.log_dir != None:
log_dir = os.path.expanduser(args.log_dir) + excute_name
eval_log_dir = log_dir + "_eval"
utils.cleanup_log_dir(log_dir + "_env1")
utils.cleanup_log_dir(log_dir + "_env2")
utils.cleanup_log_dir(log_dir + "_env3")
utils.cleanup_log_dir(log_dir + "_env4")
torch.set_num_threads(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
envs1 = make_vec_envs(env_name1, args.seed, args.num_processes, args.gamma, log_dir + "_env1", device, False, use_rms=False)
envs2 = make_vec_envs(env_name2, args.seed, args.num_processes, args.gamma, log_dir + "_env2", device, False, use_rms=False)
envs3 = make_vec_envs(env_name3, args.seed, args.num_processes, args.gamma, log_dir + "_env3", device, False, use_rms=False)
envs4 = make_vec_envs(env_name4, args.seed, args.num_processes, args.gamma, log_dir + "_env4", device, False, use_rms=False)
if not args.no_rms:
total_obs_rms = RunningMeanStd(shape=envs1.observation_space.shape)
def total_obsfilt(obs, update=False):
return np.clip((obs - total_obs_rms.mean) / np.sqrt(total_obs_rms.var + 1e-8), -10.0, 10.0)
actor_critic = Policy(
envs1.observation_space.shape,
envs1.action_space,
base_kwargs={'recurrent': args.recurrent_policy})
actor_critic.to(device)
agent = algo.PPO(actor_critic, args.clip_param, args.ppo_epoch, args.num_mini_batch, args.value_loss_coef,
lr=args.lr, eps=args.eps, max_grad_norm=args.max_grad_norm)
assert len(envs1.observation_space.shape) == 1
assert len(envs2.observation_space.shape) == 1
assert len(envs3.observation_space.shape) == 1
assert len(envs4.observation_space.shape) == 1
if "rime+wsd" in args.algo_name.lower():
discr1 = gail.WeightShared_discriminator(4, envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr2 = gail.WeightShared_discriminator(4, envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr3 = gail.WeightShared_discriminator(4, envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr4 = gail.WeightShared_discriminator(4, envs4.observation_space.shape[0] + envs4.action_space.shape[0], 100, device)
elif (("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower())) or "omme" in args.algo_name.lower():
discr11 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr12 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr13 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr14 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr21 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr22 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr23 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr24 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr31 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr32 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr33 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr34 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr41 = gail.Discriminator(envs4.observation_space.shape[0] + envs4.action_space.shape[0], 100, device)
discr42 = gail.Discriminator(envs4.observation_space.shape[0] + envs4.action_space.shape[0], 100, device)
discr43 = gail.Discriminator(envs4.observation_space.shape[0] + envs4.action_space.shape[0], 100, device)
discr44 = gail.Discriminator(envs4.observation_space.shape[0] + envs4.action_space.shape[0], 100, device)
elif "mixture" in args.algo_name.lower():
discr = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
elif "single" in args.algo_name.lower():
discr1 = gail.Discriminator(envs1.observation_space.shape[0] + envs1.action_space.shape[0], 100, device)
discr2 = gail.Discriminator(envs2.observation_space.shape[0] + envs2.action_space.shape[0], 100, device)
discr3 = gail.Discriminator(envs3.observation_space.shape[0] + envs3.action_space.shape[0], 100, device)
discr4 = gail.Discriminator(envs4.observation_space.shape[0] + envs4.action_space.shape[0], 100, device)
else:
print("Check algo-name")
import pdb; pdb.set_trace()
file_name1 = "./expert_demonstrations/" + args.expert_path1 + ".pt"
file_name2 = "./expert_demonstrations/" + args.expert_path2 + ".pt"
file_name3 = "./expert_demonstrations/" + args.expert_path3 + ".pt"
file_name4 = "./expert_demonstrations/" + args.expert_path4 + ".pt"
expert_dataset1 = gail.ExpertDataset(file_name1, num_trajectories=0, subsample_frequency=1)
expert_dataset2 = gail.ExpertDataset(file_name2, num_trajectories=0, subsample_frequency=1)
expert_dataset3 = gail.ExpertDataset(file_name3, num_trajectories=0, subsample_frequency=1)
expert_dataset4 = gail.ExpertDataset(file_name4, num_trajectories=0, subsample_frequency=1)
drop_last1 = len(expert_dataset1) > args.gail_batch_size
drop_last2 = len(expert_dataset2) > args.gail_batch_size
drop_last3 = len(expert_dataset3) > args.gail_batch_size
drop_last4 = len(expert_dataset4) > args.gail_batch_size
gail_train_loader1 = torch.utils.data.DataLoader(dataset=expert_dataset1, batch_size=args.gail_batch_size, shuffle=True, drop_last=drop_last1)
gail_train_loader2 = torch.utils.data.DataLoader(dataset=expert_dataset2, batch_size=args.gail_batch_size, shuffle=True, drop_last=drop_last2)
gail_train_loader3 = torch.utils.data.DataLoader(dataset=expert_dataset3, batch_size=args.gail_batch_size, shuffle=True, drop_last=drop_last3)
gail_train_loader4 = torch.utils.data.DataLoader(dataset=expert_dataset4, batch_size=args.gail_batch_size, shuffle=True, drop_last=drop_last4)
rollouts1 = RolloutStorage(args.num_steps, args.num_processes, envs1.observation_space.shape, envs1.action_space, actor_critic.recurrent_hidden_state_size)
rollouts2 = RolloutStorage(args.num_steps, args.num_processes, envs2.observation_space.shape, envs2.action_space, actor_critic.recurrent_hidden_state_size)
rollouts3 = RolloutStorage(args.num_steps, args.num_processes, envs3.observation_space.shape, envs3.action_space, actor_critic.recurrent_hidden_state_size)
rollouts4 = RolloutStorage(args.num_steps, args.num_processes, envs4.observation_space.shape, envs4.action_space, actor_critic.recurrent_hidden_state_size)
obs1 = envs1.reset()
obs2 = envs2.reset()
obs3 = envs3.reset()
obs4 = envs4.reset()
if not args.no_rms:
total_obs_rms.update(obs1)
total_obs_rms.update(obs2)
total_obs_rms.update(obs3)
total_obs_rms.update(obs4)
obs1 = torch.from_numpy(obs1).float().to(device)
obs2 = torch.from_numpy(obs2).float().to(device)
obs3 = torch.from_numpy(obs3).float().to(device)
obs4 = torch.from_numpy(obs4).float().to(device)
rollouts1.obs[0].copy_(obs1)
rollouts1.to(device)
rollouts2.obs[0].copy_(obs2)
rollouts2.to(device)
rollouts3.obs[0].copy_(obs3)
rollouts3.to(device)
rollouts4.obs[0].copy_(obs4)
rollouts4.to(device)
episode_rewards1 = deque(maxlen=20)
episode_rewards2 = deque(maxlen=20)
episode_rewards3 = deque(maxlen=20)
episode_rewards4 = deque(maxlen=20)
num_updates = int(args.num_env_steps) // args.num_steps // args.num_processes
for j in range(num_updates):
if args.use_linear_lr_decay:
utils.update_linear_schedule(agent.optimizer, j, num_updates, args.lr)
for step in range(args.num_steps):
# Sample actions
with torch.no_grad():
value1, action1, action_log_prob1, recurrent_hidden_states1 = actor_critic.act(rollouts1.obs[step],
rollouts1.recurrent_hidden_states[step],
rollouts1.masks[step])
value2, action2, action_log_prob2, recurrent_hidden_states2 = actor_critic.act(rollouts2.obs[step],
rollouts2.recurrent_hidden_states[step],
rollouts2.masks[step])
value3, action3, action_log_prob3, recurrent_hidden_states3 = actor_critic.act(rollouts3.obs[step],
rollouts3.recurrent_hidden_states[step],
rollouts3.masks[step])
value4, action4, action_log_prob4, recurrent_hidden_states4 = actor_critic.act(rollouts4.obs[step],
rollouts4.recurrent_hidden_states[step],
rollouts4.masks[step])
# Obser reward and next obs
obs1, _, done1, infos1 = envs1.step(action1)
obs2, _, done2, infos2 = envs2.step(action2)
obs3, _, done3, infos3 = envs3.step(action3)
obs4, _, done4, infos4 = envs4.step(action4)
if not args.no_rms:
temp_obs1 = obs1
temp_obs2 = obs2
temp_obs3 = obs3
temp_obs4 = obs4
obs1 = total_obsfilt(obs1)
obs2 = total_obsfilt(obs2)
obs3 = total_obsfilt(obs3)
obs4 = total_obsfilt(obs4)
if j < args.rms_iters + 1:
total_obs_rms.update(temp_obs1)
total_obs_rms.update(temp_obs2)
total_obs_rms.update(temp_obs3)
total_obs_rms.update(temp_obs4)
obs1 = torch.from_numpy(obs1).float().to(device)
obs2 = torch.from_numpy(obs2).float().to(device)
obs3 = torch.from_numpy(obs3).float().to(device)
obs4 = torch.from_numpy(obs4).float().to(device)
for info1 in infos1:
if 'episode' in info1.keys():
episode_rewards1.append(info1['episode']['r'])
for info2 in infos2:
if 'episode' in info2.keys():
episode_rewards2.append(info2['episode']['r'])
for info3 in infos3:
if 'episode' in info3.keys():
episode_rewards3.append(info3['episode']['r'])
for info4 in infos4:
if 'episode' in info4.keys():
episode_rewards4.append(info4['episode']['r'])
# If done then clean the history of observations.
masks1 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done1])
masks2 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done2])
masks3 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done3])
masks4 = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done4])
bad_masks1 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos1])
bad_masks2 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos2])
bad_masks3 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos3])
bad_masks4 = torch.FloatTensor([[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos4])
rollouts1.insert(obs1, recurrent_hidden_states1, action1, action_log_prob1, value1, torch.zeros(1, 1), masks1, bad_masks1)
rollouts2.insert(obs2, recurrent_hidden_states2, action2, action_log_prob2, value2, torch.zeros(1, 1), masks2, bad_masks2)
rollouts3.insert(obs3, recurrent_hidden_states3, action3, action_log_prob3, value3, torch.zeros(1, 1), masks3, bad_masks3)
rollouts4.insert(obs4, recurrent_hidden_states4, action4, action_log_prob4, value4, torch.zeros(1, 1), masks4, bad_masks4)
with torch.no_grad():
next_value1 = actor_critic.get_value(rollouts1.obs[-1], rollouts1.recurrent_hidden_states[-1], rollouts1.masks[-1]).detach()
next_value2 = actor_critic.get_value(rollouts2.obs[-1], rollouts2.recurrent_hidden_states[-1], rollouts2.masks[-1]).detach()
next_value3 = actor_critic.get_value(rollouts3.obs[-1], rollouts3.recurrent_hidden_states[-1], rollouts3.masks[-1]).detach()
next_value4 = actor_critic.get_value(rollouts4.obs[-1], rollouts4.recurrent_hidden_states[-1], rollouts4.masks[-1]).detach()
if j >= args.rms_iters:
envs1.venv.eval()
envs2.venv.eval()
envs3.venv.eval()
envs4.venv.eval()
gail_epoch = args.gail_epoch
if j < args.rms_iters:
gail_epoch = args.initial_dstep # Warm up
for _ in range(gail_epoch):
if "rime+wsd" in args.algo_name.lower():
discr1.update_WSD_4env(rollouts1, gail_train_loader1, gail_train_loader2, gail_train_loader3, gail_train_loader4, rms=total_obs_rms)
discr2.update_WSD_4env(rollouts2, gail_train_loader1, gail_train_loader2, gail_train_loader3, gail_train_loader4, rms=total_obs_rms)
discr3.update_WSD_4env(rollouts3, gail_train_loader1, gail_train_loader2, gail_train_loader3, gail_train_loader4, rms=total_obs_rms)
discr4.update_WSD_4env(rollouts4, gail_train_loader1, gail_train_loader2, gail_train_loader3, gail_train_loader4, rms=total_obs_rms)
elif (("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower())) or "omme" in args.algo_name.lower():
discr11.update_gp(gail_train_loader1, rollouts1, total_obs_rms)
discr12.update_gp(gail_train_loader2, rollouts1, total_obs_rms)
discr13.update_gp(gail_train_loader3, rollouts1, total_obs_rms)
discr14.update_gp(gail_train_loader4, rollouts1, total_obs_rms)
discr21.update_gp(gail_train_loader1, rollouts2, total_obs_rms)
discr22.update_gp(gail_train_loader2, rollouts2, total_obs_rms)
discr23.update_gp(gail_train_loader3, rollouts2, total_obs_rms)
discr24.update_gp(gail_train_loader4, rollouts2, total_obs_rms)
discr31.update_gp(gail_train_loader1, rollouts3, total_obs_rms)
discr32.update_gp(gail_train_loader2, rollouts3, total_obs_rms)
discr33.update_gp(gail_train_loader3, rollouts3, total_obs_rms)
discr34.update_gp(gail_train_loader4, rollouts3, total_obs_rms)
discr41.update_gp(gail_train_loader1, rollouts4, total_obs_rms)
discr42.update_gp(gail_train_loader2, rollouts4, total_obs_rms)
discr43.update_gp(gail_train_loader3, rollouts4, total_obs_rms)
discr44.update_gp(gail_train_loader4, rollouts4, total_obs_rms)
elif "mixture" in args.algo_name.lower():
discr.update_gp_mixture_4envs(gail_train_loader1, gail_train_loader2, gail_train_loader3, gail_train_loader4, rollouts1, rollouts2, rollouts3, rollouts4, total_obs_rms)
elif "single" in args.algo_name.lower():
discr1.update_gp(gail_train_loader1, rollouts1, total_obs_rms)
discr2.update_gp(gail_train_loader2, rollouts2, total_obs_rms)
discr3.update_gp(gail_train_loader3, rollouts3, total_obs_rms)
discr4.update_gp(gail_train_loader4, rollouts4, total_obs_rms)
roll_temp_1 = [[],[],[],[]]
roll_temp_2 = [[],[],[],[]]
roll_temp_3 = [[],[],[],[]]
roll_temp_4 = [[],[],[],[]]
for step in range(args.num_steps):
if "rime+wsd" in args.algo_name.lower():
rollouts1.rewards[step] = torch.min(torch.cat([discr1.predict_reward(0, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr1.predict_reward(1, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr1.predict_reward(2, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr1.predict_reward(3, rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])]))
rollouts2.rewards[step] = torch.min(torch.cat([discr2.predict_reward(0, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr2.predict_reward(1, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr2.predict_reward(2, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr2.predict_reward(3, rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])]))
rollouts3.rewards[step] = torch.min(torch.cat([discr3.predict_reward(0, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr3.predict_reward(1, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr3.predict_reward(2, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr3.predict_reward(3, rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])]))
rollouts4.rewards[step] = torch.min(torch.cat([discr4.predict_reward(0, rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]),
discr4.predict_reward(1, rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]),
discr4.predict_reward(2, rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]),
discr4.predict_reward(3, rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step])]))
elif ("rime" in args.algo_name.lower()) and ("wsd" not in args.algo_name.lower()):
rollouts1.rewards[step] = torch.min(torch.cat([discr11.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr12.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr13.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]),
discr14.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])]))
rollouts2.rewards[step] = torch.min(torch.cat([discr21.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr22.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr23.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]),
discr24.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])]))
rollouts3.rewards[step] = torch.min(torch.cat([discr31.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr32.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr33.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]),
discr34.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])]))
rollouts4.rewards[step] = torch.min(torch.cat([discr41.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]),
discr42.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]),
discr43.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]),
discr44.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step])]))
elif "omme" in args.algo_name.lower():
roll_temp_1[0].append(discr11.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_1[1].append(discr12.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_1[2].append(discr13.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_1[3].append(discr14.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step]))
roll_temp_2[0].append(discr21.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_2[1].append(discr22.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_2[2].append(discr23.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_2[3].append(discr24.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step]))
roll_temp_3[0].append(discr31.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
roll_temp_3[1].append(discr32.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
roll_temp_3[2].append(discr33.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
roll_temp_3[3].append(discr34.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step]))
roll_temp_4[0].append(discr41.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]))
roll_temp_4[1].append(discr42.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]))
roll_temp_4[2].append(discr43.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]))
roll_temp_4[3].append(discr44.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step]))
elif "mixture" in args.algo_name.lower():
rollouts1.rewards[step] = discr.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])
rollouts2.rewards[step] = discr.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])
rollouts3.rewards[step] = discr.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])
rollouts4.rewards[step] = discr.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step])
elif "single" in args.algo_name.lower():
rollouts1.rewards[step] = discr1.predict_reward(rollouts1.obs[step], rollouts1.actions[step], args.gamma, rollouts1.masks[step])
rollouts2.rewards[step] = discr2.predict_reward(rollouts2.obs[step], rollouts2.actions[step], args.gamma, rollouts2.masks[step])
rollouts3.rewards[step] = discr3.predict_reward(rollouts3.obs[step], rollouts3.actions[step], args.gamma, rollouts3.masks[step])
rollouts4.rewards[step] = discr4.predict_reward(rollouts4.obs[step], rollouts4.actions[step], args.gamma, rollouts4.masks[step])
if "omme" in args.algo_name.lower():
index_roll1 = np.argmin([torch.mean(torch.tensor(roll_temp_1[0])), torch.mean(torch.tensor(roll_temp_1[1])),
torch.mean(torch.tensor(roll_temp_1[2])), torch.mean(torch.tensor(roll_temp_1[3]))])
index_roll2 = np.argmin([torch.mean(torch.tensor(roll_temp_2[0])), torch.mean(torch.tensor(roll_temp_2[1])),
torch.mean(torch.tensor(roll_temp_2[2])), torch.mean(torch.tensor(roll_temp_2[3]))])
index_roll3 = np.argmin([torch.mean(torch.tensor(roll_temp_3[0])), torch.mean(torch.tensor(roll_temp_3[1])),
torch.mean(torch.tensor(roll_temp_3[2])), torch.mean(torch.tensor(roll_temp_3[3]))])
index_roll4 = np.argmin([torch.mean(torch.tensor(roll_temp_4[0])), torch.mean(torch.tensor(roll_temp_4[1])),
torch.mean(torch.tensor(roll_temp_4[2])), torch.mean(torch.tensor(roll_temp_4[3]))])
for step in range(args.num_steps):
rollouts1.rewards[step] = roll_temp_1[index_roll1][step]
rollouts2.rewards[step] = roll_temp_2[index_roll2][step]
rollouts3.rewards[step] = roll_temp_3[index_roll3][step]
rollouts4.rewards[step] = roll_temp_4[index_roll4][step]
rollouts1.compute_returns(next_value1, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
rollouts2.compute_returns(next_value2, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
rollouts3.compute_returns(next_value3, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
rollouts4.compute_returns(next_value4, args.use_gae, args.gamma, args.gae_lambda, args.use_proper_time_limits)
_, _, _ = agent.update_4envs(rollouts1, rollouts2, rollouts3, rollouts4)
rollouts1.after_update()
rollouts2.after_update()
rollouts3.after_update()
rollouts4.after_update()
if len(episode_rewards1) > 1 and len(episode_rewards2) > 1 and len(episode_rewards3) > 1 and len(episode_rewards4) > 1:
total_num_steps = (j + 1) * args.num_processes * args.num_steps
print("Updates {}, num timesteps {} \n Last {} training episodes \n mean/median reward1 {:.1f}/{:.1f}, reward2 {:.1f}/{:.1f}, reward3 {:.1f}/{:.1f}, reward4 {:.1f}/{:.1f} \n min/max reward1 {:.1f}/{:.1f}, reward2 {:.1f}/{:.1f}, reward3 {:.1f}/{:.1f}, reward4 {:.1f}/{:.1f}\n"
.format(j, total_num_steps, len(episode_rewards1),
np.mean(episode_rewards1), np.median(episode_rewards1),
np.mean(episode_rewards2), np.median(episode_rewards2),
np.mean(episode_rewards3), np.median(episode_rewards3),
np.mean(episode_rewards4), np.median(episode_rewards4),
np.min(episode_rewards1), np.max(episode_rewards1),
np.min(episode_rewards2), np.max(episode_rewards2),
np.min(episode_rewards3), np.max(episode_rewards3),
np.min(episode_rewards4), np.max(episode_rewards4)))
if (len(episode_rewards1) > 1) and (len(episode_rewards2) > 1) and (len(episode_rewards3) > 1) and (len(episode_rewards4) > 1):
perfs_2par_gm = []
env_par_list_2par = ['050g150m', '070g150m', '090g150m', '110g150m', '130g150m', '150g150m',
'050g130m', '070g130m', '090g130m', '110g130m', '130g130m', '150g130m',
'050g110m', '070g110m', '090g110m', '110g110m', '130g110m', '150g110m',
'050g090m', '070g090m', '090g090m', '110g090m', '130g090m', '150g090m',
'050g070m', '070g070m', '090g070m', '110g070m', '130g070m', '150g070m',
'050g050m', '070g050m', '090g050m', '110g050m', '130g050m', '150g050m']
env_list_gm = []
env_n = args.env_name.split("-")
for par in env_par_list_2par:
env_list_gm.append(env_n[0] + par + '-' + env_n[1])
for env_name in env_list_gm:
perfs_2par_gm.append(evaluate(actor_critic, total_obs_rms, env_name, args.seed, args.num_processes, eval_log_dir, device))
return [perfs_2par_gm, excute_name]
if __name__ == "__main__":
args = get_args()
if args.sampled_envs == 2:
learn_the_2_sampled_envs(args)
elif args.sampled_envs == 3:
learn_the_3_sampled_envs(args)
elif args.sampled_envs == 4:
learn_the_4_sampled_envs(args)