def _create_replay_buffer_and_insert(env: EnvWrapper):
env.seed(1)
replay_buffer = ReplayBuffer(replay_capacity=6, batch_size=1)
replay_buffer_inserter = make_replay_buffer_inserter(env)
obs = env.reset()
inserted = []
terminal = False
i = 0
while not terminal and i < 5:
logger.info(f"Iteration: {i}")
action = env.action_space.sample()
next_obs, reward, terminal, _ = env.step(action)
inserted.append({
"observation": obs,
"action": action,
"reward": reward,
"terminal": terminal,
})
transition = Transition(
mdp_id=0,
sequence_number=i,
observation=obs,
action=action,
reward=reward,
terminal=terminal,
log_prob=0.0,
)
replay_buffer_inserter(replay_buffer, transition)
obs = next_obs
i += 1
return replay_buffer, inserted
def run_episode(env: EnvWrapper,
agent: Agent,
mdp_id: int = 0,
max_steps: Optional[int] = None) -> Trajectory:
"""
Return sum of rewards from episode.
After max_steps (if specified), the environment is assumed to be terminal.
Can also specify the mdp_id and gamma of episode.
"""
trajectory = Trajectory()
obs = env.reset()
possible_actions_mask = env.possible_actions_mask
terminal = False
num_steps = 0
while not terminal:
action, log_prob = agent.act(obs, possible_actions_mask)
next_obs, reward, terminal, _ = env.step(action)
next_possible_actions_mask = env.possible_actions_mask
if max_steps is not None and num_steps >= max_steps:
terminal = True
# Only partially filled. Agent can fill in more fields.
transition = Transition(
mdp_id=mdp_id,
sequence_number=num_steps,
observation=obs,
action=action,
reward=float(reward),
terminal=bool(terminal),
log_prob=log_prob,
possible_actions_mask=possible_actions_mask,
)
agent.post_step(transition)
trajectory.add_transition(transition)
SummaryWriterContext.increase_global_step()
obs = next_obs
possible_actions_mask = next_possible_actions_mask
num_steps += 1
agent.post_episode(trajectory)
return trajectory
