def make_vec_env(make_env, num_env, init_seed):
r"""Create a vectorized environment, each associated with a different random seed.
Example::
>>> import gym
>>> make_vec_env(lambda: gym.make('CartPole-v1'), 3, 0)
<VecEnv: 3, CartPole-v1>
Args:
make_env (function): a function to create an environment
num_env (int): number of environments to create.
init_seed (int): initial seed for :class:`Seeder` to sample random seeds.
Returns
-------
env : VecEnv
created vectorized environment
"""
# Generate different seeds for each environment
seeder = Seeder(init_seed=init_seed)
seeds = seeder(size=num_env)
def f(seed):
env = make_env()
env.seed(seed)
env.observation_space.seed(seed)
env.action_space.seed(seed)
return env
# Use partial to generate a list of argument-free make_env, each with different seed
list_make_env = [partial(f, seed=seed) for seed in seeds]
return VecEnv(list_make_env)
def test_make_vec_env(env_id, num_env, init_seed):
def make_env():
return gym.make(env_id)
env = make_vec_env(make_env, num_env, init_seed)
assert isinstance(env, VecEnv)
seeds = [x.keywords['seed'] for x in env.list_make_env]
seeder = Seeder(init_seed)
assert seeds == seeder(num_env)
def test_episode_runner(vec_env, env_id):
env = make_vec_env(vec_env, make_gym_env, env_id, 3, 0)
env_spec = EnvSpec(env)
if env_id == 'CartPole-v1':
sticky_action = 1
elif env_id == 'Pendulum-v0':
sticky_action = [0.1]
T = 30
agent = StickyAgent(None, env_spec, sticky_action)
runner = EpisodeRunner(None, agent, env)
D = runner(T)
assert D.N == 3
assert D.maxT == max(D.Ts)
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(T):
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 test_seeder():
seeder = Seeder(init_seed=0)
assert seeder.rng.get_state()[1][0] == 0
assert np.random.get_state()[1][20] != seeder.rng.get_state()[1][20]
# Single list of seeds
seeds = seeder(size=1)
assert len(seeds) == 1
seeds = seeder(size=5)
assert len(seeds) == 5
# Batched seeds
seeds = seeder(size=[1, 3])
assert np.alltrue(np.array(seeds).shape == (1, 3))
seeds = seeder(size=[2, 3])
assert np.alltrue(np.array(seeds).shape == (2, 3))
def test_make_envs(self):
list_make_env = make_envs(make_env=make_gym_env,
env_id='Pendulum-v0',
num_env=3,
init_seed=1)
assert len(list_make_env) == 3
assert list_make_env[0] != list_make_env[1] and list_make_env[
0] != list_make_env[2]
# Test if the seedings are correct
seeder = Seeder(init_seed=1)
seeds = seeder(3)
for make_env, seed in zip(list_make_env, seeds):
assert make_env.keywords['seed'] == seed
env = list_make_env[0]()
raw_env = gym.make('Pendulum-v0')
raw_env.seed(seeds[0])
assert np.allclose(env.reset(), raw_env.reset())
def test_rolling_segment_runner(vec_env, env_id):
env = make_vec_env(vec_env, make_gym_env, env_id, 3, 0)
env_spec = EnvSpec(env)
if env_id == 'CartPole-v1':
sticky_action = 1
elif env_id == 'Pendulum-v0':
sticky_action = [0.1]
T = 30
agent = StickyAgent(None, env_spec, sticky_action)
runner = RollingSegmentRunner(None, agent, env)
D = runner(T)
assert D.N == 3
assert D.T == T
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.numpy_observations[n, 0, ...])
for t in range(T):
obs, reward, done, info = ev.step(sticky_action)
if done:
info['terminal_observation'] = obs
obs = ev.reset()
assert np.allclose(obs, D.numpy_observations[n, t + 1, ...])
assert np.allclose(sticky_action, D.numpy_actions[n, t, ...])
assert np.allclose(reward, D.numpy_rewards[n, t])
assert done == D.numpy_dones[n, t]
assert int(not done) == D.numpy_masks[n, t]
if done:
assert np.allclose(info['terminal_observation'],
D.infos[n][t]['terminal_observation'])
def algorithm(config, seed, device):
logdir = Path(config['log.dir']) / str(config['ID']) / str(seed)
seeder = Seeder(seed)
seeds = seeder(size=config['env.count'])
env_constructors = []
for seed in seeds:
env_constructors.append(partial(CraftingEnv, seed))
env = VecStandardize(SerialVecEnv(env_constructors),
clip_reward=100.0)
env_spec = EnvSpec(env)
agent = Agent(config, env_spec, device)
runner = RollingSegmentRunner(config, agent, env)
engine = Engine(agent, runner, env)
for i in range(config['train.iter']):
training_result = engine.train(i)
print(f'Training iteration {i} complete.')
if i % config['log.interval'] == 0:
logs = engine.log_train(training_result)
pickle_dump(obj=logs, f=logdir / f'iter_{i}_train_logs', ext='.pkl')
torch.save(engine.agent.policy.state_dict(),
logdir / 'trained_params')
def make_envs(make_env, env_id, num_env, init_seed, **kwargs):
r"""Create a list of argument-free make_env() functions based on the given settings.
.. note::
Each make_env function in the list uses different random seeds generated by :class:`Seeder`.
Example::
>>> make_envs(make_env=make_gym_env, env_id='CartPole-v1', num_env=3, init_seed=0)
[functools.partial(<function make_gym_env at 0x7f2127b5ce18>, env_id='CartPole-v1', seed=209652396),
functools.partial(<function make_gym_env at 0x7f2127b5ce18>, env_id='CartPole-v1', seed=398764591),
functools.partial(<function make_gym_env at 0x7f2127b5ce18>, env_id='CartPole-v1', seed=924231285)]
Args:
make_env (function): a function to create an environment
env_id (str): environment ID, e.g. 'Pendulum-v0', 'Ant-v2'
num_env (int): number of environments to create.
init_seed (int): initial seed for :class:`Seeder` to sample random seeds.
**kwargs: keyword aguments used to specify other options for make_env.
Returns
-------
list_make_env : list
a list of argument-free make_env() functions, each associated with different random seed.
"""
# Generate different seeds for each environment
seeder = Seeder(init_seed=init_seed)
seeds = seeder(size=num_env)
# Use partial to generate a list of argument-free make_env, each with different seed
list_make_env = [
partial(make_env, env_id=env_id, seed=seed, **kwargs) for seed in seeds
]
return list_make_env
import torch
import pickle
params = torch.load('logs-3/0/1013845395/trained_params')
from dial_control_rl import agent
from dial_control_rl.env import CraftingEnv
from lagom.utils import Seeder
from lagom.envs.vec_env import SerialVecEnv, VecStandardize
from lagom.envs import EnvSpec
from functools import partial
env = CraftingEnv()
seeder = Seeder(0)
seeds = seeder(size=1)
env_constructors = []
for seed in seeds:
env_constructors.append(partial(CraftingEnv, seed))
env = VecStandardize(SerialVecEnv(env_constructors), clip_reward=100.0)
env_spec = EnvSpec(env)
policy = agent.Policy({'algo.rl': 0}, env_spec, torch.device('cpu'))
policy.load_state_dict(params)
policy = policy.double()
def V(x):
out = policy(torch.tensor(x), ['V'])
return out['V'][0]
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 test_batch_segment(vec_env, env_id):
env = make_vec_env(vec_env, make_gym_env, env_id, 3, 0)
env_spec = EnvSpec(env)
T = 30
D = BatchSegment(env_spec, T)
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(0, obs)
for t in range(T):
action = [sticky_action] * env.num_env
obs, reward, done, info = env.step(action)
D.add_observation(t + 1, obs)
D.add_action(t, action)
D.add_reward(t, reward)
D.add_done(t, done)
D.add_info(info)
D.add_batch_info({'V': [0.1 * (t + 1), (t + 1), 10 * (t + 1)]})
assert D.N == 3
assert D.T == T
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 D.numpy_actions.dtype == action_dtype
assert D.numpy_dones.dtype == np.bool
assert D.numpy_observations.shape[:2] == (3, T + 1)
assert D.numpy_actions.shape == (3, T) + action_shape
assert all([
x.shape == (3, T)
for x in [D.numpy_rewards, D.numpy_dones, D.numpy_masks]
])
assert isinstance(D.batch_infos, list) and len(D.batch_infos) == T
assert np.allclose([0.1 * (x + 1) for x in range(T)],
[info['V'][0] for info in D.batch_infos])
assert np.allclose([1 * (x + 1) for x in range(T)],
[info['V'][1] for info in D.batch_infos])
assert np.allclose([10 * (x + 1) for x in range(T)],
[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.numpy_observations[n, 0, ...])
for t in range(T):
obs, reward, done, info = ev.step(sticky_action)
if done:
info['terminal_observation'] = obs
obs = ev.reset()
assert np.allclose(obs, D.numpy_observations[n, t + 1, ...])
assert np.allclose(sticky_action, D.numpy_actions[n, t, ...])
assert np.allclose(reward, D.numpy_rewards[n, t])
assert done == D.numpy_dones[n, t]
assert int(not done) == D.numpy_masks[n, t]
if done:
assert np.allclose(info['terminal_observation'],
D.infos[n][t]['terminal_observation'])