[RLlib] Terminated/Truncated in Quickstart Script. (ray-project#31386)

rllib/examples/documentation/rllib_on_ray_readme.py

@@ -24,28 +24,33 @@ def __init__(self, config):
  self.observation_space = gym.spaces.Box(0.0, self.end_pos, shape=(1,))
 
  def reset(self, *, seed=None, options=None):
- """Resets the episode and returns the initial observation of the new one."""
+ """Resets the episode.
+
+ Returns:
+ Initial observation of the new episode and an info dict.
+ """
  self.cur_pos = 0
  # Return initial observation.
  return [self.cur_pos], {}
 
  def step(self, action):
- """Takes a single step in the episode given `action`
+ """Takes a single step in the episode given `action`.
 
  Returns:
- New observation, reward, done-flag, info-dict (empty).
+ New observation, reward, terminated-flag, truncated-flag, info-dict (empty).
  """
  # Walk left.
  if action == 0 and self.cur_pos > 0:
  self.cur_pos -= 1
  # Walk right.
  elif action == 1:
  self.cur_pos += 1
- # Set `done` and `truncated` flags when end of corridor (goal) reached.
- done = truncated = self.cur_pos >= self.end_pos
+ # Set `terminated` flag when end of corridor (goal) reached.
+ terminated = self.cur_pos >= self.end_pos
+ truncated = False
  # +1 when goal reached, otherwise -1.
- reward = 1.0 if done else -0.1
- return [self.cur_pos], reward, done, truncated, {}
+ reward = 1.0 if terminated else -0.1
+ return [self.cur_pos], reward, terminated, truncated, {}
 
 
 # Create an RLlib Algorithm instance from a PPOConfig object.
@@ -78,15 +83,15 @@ def step(self, action):
 env = SimpleCorridor({"corridor_length": 10})
 # Get the initial observation (should be: [0.0] for the starting position).
 obs, info = env.reset()
-done = False
+terminated = truncated = False
 total_reward = 0.0
 # Play one episode.
-while not done:
+while not terminated and not truncated:
  # Compute a single action, given the current observation
  # from the environment.
  action = algo.compute_single_action(obs)
  # Apply the computed action in the environment.
- obs, reward, done, truncated, info = env.step(action)
+ obs, reward, terminated, truncated, info = env.step(action)
  # Sum up rewards for reporting purposes.
  total_reward += reward
 # Report results.