def __call__(self, T):
D = BatchEpisode(self.env_spec)
obs = self.env.reset()
D.add_observation(obs)
self.agent.reset(self.config) # e.g. RNN initial states
done = None # for RNN mask
for t in range(T):
if self.agent.recurrent and done is not None and any(done):
kwargs = {
'mask':
torch.from_numpy(np.logical_not(done).astype(np.float32))
}
else:
kwargs = {}
out_agent = self.agent.choose_action(obs, **kwargs)
action = out_agent.pop('action')
if torch.is_tensor(action):
raw_action = list(action.detach().cpu().numpy())
else:
raw_action = action
D.add_action(raw_action)
obs, reward, done, info = self.env.step(raw_action)
D.add_observation(obs)
D.add_reward(reward)
D.add_done(done)
D.add_info(info)
[D.set_completed(n) for n, d in enumerate(done) if d]
# Record other information: e.g. log-probability of action, policy entropy
D.add_batch_info(out_agent)
if all(D.completes):
break
return D
def test_batch_episode(vec_env, env_id):
env = make_vec_env(vec_env, make_gym_env, env_id, 3, 0)
env_spec = EnvSpec(env)
D = BatchEpisode(env_spec)
if env_id == 'CartPole-v1':
sticky_action = 1
action_shape = ()
action_dtype = np.int32
elif env_id == 'Pendulum-v0':
sticky_action = [0.1]
action_shape = env_spec.action_space.shape
action_dtype = np.float32
obs = env.reset()
D.add_observation(obs)
for t in range(30):
action = [sticky_action] * env.num_env
obs, reward, done, info = env.step(action)
D.add_observation(obs)
D.add_action(action)
D.add_reward(reward)
D.add_done(done)
D.add_info(info)
D.add_batch_info({'V': [0.1 * (t + 1), (t + 1), 10 * (t + 1)]})
[D.set_completed(n) for n, d in enumerate(done) if d]
assert D.N == 3
assert len(D.Ts) == 3
assert D.maxT == max(D.Ts)
assert all([
isinstance(x, np.ndarray) for x in [
D.numpy_observations, D.numpy_actions, D.numpy_rewards,
D.numpy_dones, D.numpy_masks
]
])
assert all([
x.dtype == np.float32
for x in [D.numpy_observations, D.numpy_rewards, D.numpy_masks]
])
assert all([
x.shape == (3, D.maxT)
for x in [D.numpy_rewards, D.numpy_dones, D.numpy_masks]
])
assert D.numpy_actions.dtype == action_dtype
assert D.numpy_dones.dtype == np.bool
assert D.numpy_observations.shape == (3, D.maxT +
1) + env_spec.observation_space.shape
assert D.numpy_actions.shape == (3, D.maxT) + action_shape
assert isinstance(D.batch_infos, list) and len(D.batch_infos) == 30
assert np.allclose([0.1 * (x + 1) for x in range(30)],
[info['V'][0] for info in D.batch_infos])
assert np.allclose([1 * (x + 1) for x in range(30)],
[info['V'][1] for info in D.batch_infos])
assert np.allclose([10 * (x + 1) for x in range(30)],
[info['V'][2] for info in D.batch_infos])
seeder = Seeder(0)
seed1, seed2, seed3 = seeder(3)
env1 = make_gym_env(env_id, seed1)
env2 = make_gym_env(env_id, seed2)
env3 = make_gym_env(env_id, seed3)
for n, ev in enumerate([env1, env2, env3]):
obs = ev.reset()
assert np.allclose(obs, D.observations[n][0])
assert np.allclose(obs, D.numpy_observations[n, 0, ...])
for t in range(30):
obs, reward, done, info = ev.step(sticky_action)
assert np.allclose(reward, D.rewards[n][t])
assert np.allclose(reward, D.numpy_rewards[n, t])
assert np.allclose(done, D.dones[n][t])
assert done == D.numpy_dones[n, t]
assert int(not done) == D.masks[n][t]
assert int(not done) == D.numpy_masks[n, t]
if done:
assert np.allclose(obs, D.infos[n][t]['terminal_observation'])
assert D.completes[n]
assert np.allclose(0.0, D.numpy_observations[n, t + 1 + 1:,
...])
assert np.allclose(0.0, D.numpy_actions[n, t + 1:, ...])
assert np.allclose(0.0, D.numpy_rewards[n, t + 1:])
assert np.allclose(True, D.numpy_dones[n, t + 1:])
assert np.allclose(0.0, D.numpy_masks[n, t + 1:])
break
else:
assert np.allclose(obs, D.observations[n][t + 1])