def eval_agent(env: py_environment.PyEnvironment,
tf_agent: agent.DQNAgent,
n_episodes: int,
reward_vector: bool = False) -> np.ndarray:
results = []
for _ in tqdm(range(n_episodes)):
ts = env.reset()
observations = ts.observation
episode_reward = 0
done = False
while not done:
action = tf_agent.greedy_policy(observations)
ts = env.step(action)
observations, reward, done = ts.observation, ts.reward, ts.is_last(
)
episode_reward += reward
assert np.isclose(episode_reward, env._prev_step_utility, atol=1e-05)
if reward_vector:
results.append([
observations['utility_representation'],
np.copy(env._cumulative_rewards)
])
else:
results.append(episode_reward)
if reward_vector:
results = np.array(results, dtype='object')
else:
results = np.array(results)
return results
def validate_py_environment(
environment: py_environment.PyEnvironment,
episodes: int = 5,
observation_and_action_constraint_splitter: Optional[
types.Splitter] = None):
"""Validates the environment follows the defined specs."""
time_step_spec = environment.time_step_spec()
action_spec = environment.action_spec()
random_policy = random_py_policy.RandomPyPolicy(
time_step_spec=time_step_spec,
action_spec=action_spec,
observation_and_action_constraint_splitter=(
observation_and_action_constraint_splitter))
if environment.batch_size is not None:
batched_time_step_spec = array_spec.add_outer_dims_nest(
time_step_spec, outer_dims=(environment.batch_size, ))
else:
batched_time_step_spec = time_step_spec
episode_count = 0
time_step = environment.reset()
while episode_count < episodes:
if not array_spec.check_arrays_nest(time_step, batched_time_step_spec):
raise ValueError('Given `time_step`: %r does not match expected '
'`time_step_spec`: %r' %
(time_step, batched_time_step_spec))
action = random_policy.action(time_step).action
time_step = environment.step(action)
episode_count += np.sum(time_step.is_last())
def validate_py_environment(environment: py_environment.PyEnvironment,
episodes: int = 5):
"""Validates the environment follows the defined specs."""
time_step_spec = environment.time_step_spec()
action_spec = environment.action_spec()
random_policy = random_py_policy.RandomPyPolicy(
time_step_spec=time_step_spec, action_spec=action_spec)
episode_count = 0
time_step = environment.reset()
while episode_count < episodes:
if not array_spec.check_arrays_nest(time_step, time_step_spec):
raise ValueError(
'Given `time_step`: %r does not match expected `time_step_spec`: %r'
% (time_step, time_step_spec))
action = random_policy.action(time_step).action
time_step = environment.step(action)
if time_step.is_last():
episode_count += 1
time_step = environment.reset()
def agent_play_episode(env: py_environment.PyEnvironment, agent: DQNAgent) -> None:
time_step = env.reset()
plt.figure(figsize=(11, 6), dpi=200)
i = 1
ax = plt.subplot(4, 8, i)
render_time_step(time_step, ax)
while not time_step.is_last():
action = agent.greedy_policy(time_step.observation)
time_step = env.step(action)
i += 1
ax = plt.subplot(4, 8, i)
render_time_step(time_step, ax, action)
plt.tight_layout()
plt.show()
