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curriculum_example.py
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curriculum_example.py
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import numpy as np
import torch
import pickle
from conformer_rl import utils
from conformer_rl.config import Config
from conformer_rl.environments import Task
from conformer_rl.models import RTGNRecurrent
from conformer_rl.molecule_generation.generate_alkanes import generate_branched_alkane
from conformer_rl.molecule_generation.generate_molecule_config import config_from_rdkit
from conformer_rl.agents import PPORecurrentExternalCurriculumAgent
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import logging
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
# Create mol_configs for the curriculum
mol_configs = [config_from_rdkit(generate_branched_alkane(i), num_conformers=200, calc_normalizers=True) for i in range(8, 16)]
eval_mol_config = config_from_rdkit(generate_branched_alkane(16), num_conformers=200, calc_normalizers=True)
config = Config()
config.tag = 'curriculum_test'
config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
# Batch Hyperparameters
config.max_steps = 100000
# training Hyperparameters
lr = 5e-6 * np.sqrt(10)
config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=lr, eps=1e-5)
# Task Settings
config.train_env = Task('GibbsScorePruningCurriculumEnv-v0', concurrency=True, num_envs=10, seed=np.random.randint(0,1e5), mol_configs=mol_configs)
config.eval_env = Task('GibbsScorePruningEnv-v0', seed=np.random.randint(0,7e4), mol_config=eval_mol_config)
config.eval_interval = 20000
# curriculum Hyperparameters
config.curriculum_agent_buffer_len = 20
config.curriculum_agent_reward_thresh = 0.4
config.curriculum_agent_success_rate = 0.7
config.curriculum_agent_fail_rate = 0.2
agent = PPORecurrentExternalCurriculumAgent(config)
agent.run_steps()