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_test_episode_buffer(
env: EnvWrapper,
policy: Policy,
trainer: Trainer,
num_train_episodes: int,
passing_score_bar: float,
num_eval_episodes: int,
use_gpu: bool = False,
):
training_policy = policy
post_episode_callback = train_post_episode(env, trainer, use_gpu)
# pyre-fixme[16]: `EnvWrapper` has no attribute `seed`.
env.seed(SEED)
# pyre-fixme[16]: `EnvWrapper` has no attribute `action_space`.
env.action_space.seed(SEED)
train_rewards = train_policy(
env,
training_policy,
num_train_episodes,
post_step=None,
post_episode=post_episode_callback,
use_gpu=use_gpu,
)
# Check whether the max score passed the score bar; we explore during training
# the return could be bad (leading to flakiness in C51 and QRDQN).
assert np.max(train_rewards) >= passing_score_bar, (
f"max reward ({np.max(train_rewards)}) after training for "
f"{len(train_rewards)} episodes is less than < {passing_score_bar}.\n")
serving_policy = policy
eval_rewards = eval_policy(env,
serving_policy,
num_eval_episodes,
serving=False)
assert (
eval_rewards.mean() >= passing_score_bar
), f"Eval reward is {eval_rewards.mean()}, less than < {passing_score_bar}.\n"