def test_final_state_from_episode():
env = make_vec_env(SerialVecEnv, make_gym_env, 'CartPole-v1', 3, 0)
env_spec = EnvSpec(env)
with pytest.raises(AssertionError):
final_state_from_episode([1, 2, 3])
D = BatchEpisode(env_spec)
D.obs[0] = [0.1, 0.2, 1.3]
D.done[0] = [False, False, True]
D.info[0] = [{}, {}, {'terminal_observation': 0.3}]
D.obs[1] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
D.done[1] = [False] * 9
D.obs[2] = [10, 15]
D.done[2] = [False, True]
D.info[2] = [{}, {'terminal_observation': 20}]
final_states = final_state_from_episode(D)
assert final_states.shape == (3, ) + env_spec.observation_space.shape
assert np.allclose(final_states[0], 0.3)
assert np.allclose(final_states[1], 9)
assert np.allclose(final_states[2], 20)
with pytest.raises(AssertionError):
final_state_from_segment(D)
def test_returns_from_episode():
env = make_vec_env(SerialVecEnv, make_gym_env, 'CartPole-v1', 3, 0)
env_spec = EnvSpec(env)
D = BatchEpisode(env_spec)
D.r[0] = [1, 2, 3]
D.done[0] = [False, False, True]
D.r[1] = [1, 2, 3, 4, 5]
D.done[1] = [False, False, False, False, False]
D.r[2] = [1, 2, 3, 4, 5, 6, 7, 8]
D.done[2] = [False, False, False, False, False, False, False, True]
out = returns_from_episode(D, 1.0)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [6, 5, 3, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [15, 14, 12, 9, 5, 0, 0, 0])
assert np.allclose(out[2], [36, 35, 33, 30, 26, 21, 15, 8])
del out
out = returns_from_episode(D, 0.1)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [1.23, 2.3, 3, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [1.2345, 2.345, 3.45, 4.5, 5, 0, 0, 0])
assert np.allclose(
out[2], [1.2345678, 2.345678, 3.45678, 4.5678, 5.678, 6.78, 7.8, 8])
with pytest.raises(AssertionError):
returns_from_segment(D, 0.1)
def test_gae_from_episode():
env = make_vec_env(SerialVecEnv, make_gym_env, 'CartPole-v1', 3, 0)
env_spec = EnvSpec(env)
D = BatchEpisode(env_spec)
D.r[0] = [1, 2, 3]
D.done[0] = [False, False, True]
D.completed[0] = True
D.r[1] = [1, 2, 3, 4, 5]
D.done[1] = [False, False, False, False, False]
D.completed[1] = False
D.r[2] = [1, 2, 3, 4, 5, 6, 7, 8]
D.done[2] = [False, False, False, False, False, False, False, True]
D.completed[2] = True
all_Vs = [
torch.tensor([[0.1], [0.5], [1.0]]),
torch.tensor([[1.1], [1.5], [2.0]]),
torch.tensor([[2.1], [2.5], [3.0]]),
torch.tensor([[3.1], [3.5], [4.0]]),
torch.tensor([[4.1], [4.5], [5.0]]),
torch.tensor([[5.1], [5.5], [6.0]]),
torch.tensor([[6.1], [6.5], [7.0]]),
torch.tensor([[7.1], [7.5], [8.0]])
]
last_Vs = torch.tensor([10, 20, 30]).unsqueeze(1)
all_Vs = torch.stack(all_Vs, 1)
out = gae_from_episode(D, all_Vs, last_Vs, 1.0, 0.5)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [3.725, 3.45, 0.9, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [6.40625, 8.8125, 11.625, 15.25, 20.5, 0, 0, 0])
assert np.allclose(out[2],
[5.84375, 7.6875, 9.375, 10.75, 11.5, 11., 8, 0.])
del out
out = gae_from_episode(D, all_Vs, last_Vs, 0.1, 0.2)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [1.03256, 1.128, 0.9, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [0.665348, 0.7674, 0.87, 1, 2.5, 0, 0, 0])
assert np.allclose(out[2], [
0.206164098, 0.308204915, 0.410245728, 0.5122864, 0.61432, 0.716, 0.8,
0
])
with pytest.raises(AssertionError):
gae_from_segment(D, all_Vs, last_Vs, 0.1, 0.2)
def test_td0_error_from_episode():
env = make_vec_env(SerialVecEnv, make_gym_env, 'CartPole-v1', 3, 0)
env_spec = EnvSpec(env)
D = BatchEpisode(env_spec)
D.r[0] = [1, 2, 3]
D.done[0] = [False, False, True]
D.completed[0] = True
D.r[1] = [1, 2, 3, 4, 5]
D.done[1] = [False, False, False, False, False]
D.completed[1] = False
D.r[2] = [1, 2, 3, 4, 5, 6, 7, 8]
D.done[2] = [False, False, False, False, False, False, False, True]
D.completed[2] = True
all_Vs = [
torch.tensor([[0.1], [0.5], [1.0]]),
torch.tensor([[1.1], [1.5], [2.0]]),
torch.tensor([[2.1], [2.5], [3.0]]),
torch.tensor([[3.1], [3.5], [4.0]]),
torch.tensor([[4.1], [4.5], [5.0]]),
torch.tensor([[5.1], [5.5], [6.0]]),
torch.tensor([[6.1], [6.5], [7.0]]),
torch.tensor([[7.1], [7.5], [8.0]])
]
last_Vs = torch.tensor([10, 20, 30]).unsqueeze(1)
all_Vs = torch.stack(all_Vs, 1)
out = td0_error_from_episode(D, all_Vs, last_Vs, 1.0)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [2.0, 3, 0.9, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [2, 3, 4, 5, 20.5, 0, 0, 0])
assert np.allclose(out[2], [2, 3, 4, 5, 6, 7, 8, 0])
del out
out = td0_error_from_episode(D, all_Vs, last_Vs, 0.1)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [1.01, 1.11, 0.9, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [0.65, 0.75, 0.85, 0.95, 2.5, 0, 0, 0])
assert np.allclose(out[2], [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0])
with pytest.raises(AssertionError):
td0_error_from_segment(D, all_Vs, last_Vs, 0.1)
def test_bootstrapped_returns_from_episode():
env = make_vec_env(SerialVecEnv, make_gym_env, 'CartPole-v1', 3, 0)
env_spec = EnvSpec(env)
D = BatchEpisode(env_spec)
D.r[0] = [1, 2, 3]
D.done[0] = [False, False, True]
D.completed[0] = True
D.r[1] = [1, 2, 3, 4, 5]
D.done[1] = [False, False, False, False, False]
D.completed[1] = False
D.r[2] = [1, 2, 3, 4, 5, 6, 7, 8]
D.done[2] = [False, False, False, False, False, False, False, True]
D.completed[2] = True
last_Vs = torch.tensor([10, 20, 30]).unsqueeze(1)
out = bootstrapped_returns_from_episode(D, last_Vs, 1.0)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [6, 5, 3, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [35, 34, 32, 29, 25, 0, 0, 0])
assert np.allclose(out[2], [36, 35, 33, 30, 26, 21, 15, 8])
del out
out = bootstrapped_returns_from_episode(D, last_Vs, 0.1)
assert out.shape == (3, D.maxT)
assert np.allclose(out[0], [1.23, 2.3, 3, 0, 0, 0, 0, 0])
assert np.allclose(out[1], [1.2347, 2.347, 3.47, 4.7, 7, 0, 0, 0])
assert np.allclose(
out[2], [1.2345678, 2.345678, 3.45678, 4.5678, 5.678, 6.78, 7.8, 8])
with pytest.raises(AssertionError):
bootstrapped_returns_from_segment(D, last_Vs, 0.1)
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])
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