def __init__(self, policy, env, verbose=0, *, requires_vec_env, policy_base):
if isinstance(policy, str):
self.policy = get_policy_from_name(policy_base, policy)
else:
self.policy = policy
self.env = env
self.verbose = verbose
self._requires_vec_env = requires_vec_env
self.observation_space = None
self.action_space = None
self.n_envs = None
self._vectorize_action = False
if env is not None:
if isinstance(env, str):
if self.verbose >= 1:
print("Creating environment from the given name, wrapped in a DummyVecEnv.")
self.env = env = DummyVecEnv([lambda: gym.make(env)])
self.observation_space = env.observation_space
self.action_space = env.action_space
if requires_vec_env:
if isinstance(env, VecEnv):
self.n_envs = env.num_envs
else:
raise ValueError("Error: the model requires a vectorized environment, please use a VecEnv wrapper.")
else:
if isinstance(env, VecEnv):
if env.num_envs == 1:
self.env = _UnvecWrapper(env)
self._vectorize_action = True
else:
raise ValueError("Error: the model requires a non vectorized environment or a single vectorized"
" environment.")
self.n_envs = 1
def __init__(self,
policy,
env,
policy_base,
policy_kwargs=None,
verbose=0,
device='auto',
support_multi_env=False,
create_eval_env=False,
monitor_wrapper=True,
seed=None):
if isinstance(policy, str) and policy_base is not None:
self.policy_class = get_policy_from_name(policy_base, policy)
else:
self.policy_class = policy
self.env = env
# get VecNormalize object if needed
self._vec_normalize_env = unwrap_vec_normalize(env)
self.verbose = verbose
self.policy_kwargs = {} if policy_kwargs is None else policy_kwargs
self.observation_space = None
self.action_space = None
self.n_envs = None
self.num_timesteps = 0
self.eval_env = None
self.replay_buffer = None
self.seed = seed
self.action_noise = None
# Track the training progress (from 1 to 0)
# this is used to update the learning rate
self._current_progress = 1
# Create and wrap the env if needed
if env is not None:
if isinstance(env, str):
if create_eval_env:
eval_env = gym.make(env)
if monitor_wrapper:
eval_env = Monitor(eval_env, filename=None)
self.eval_env = DummyVecEnv([lambda: eval_env])
if self.verbose >= 1:
print(
"Creating environment from the given name, wrapped in a DummyVecEnv."
)
env = gym.make(env)
if monitor_wrapper:
env = Monitor(env, filename=None)
env = DummyVecEnv([lambda: env])
self.observation_space = env.observation_space
self.action_space = env.action_space
if not isinstance(env, VecEnv):
if self.verbose >= 1:
print("Wrapping the env in a DummyVecEnv.")
env = DummyVecEnv([lambda: env])
self.n_envs = env.num_envs
self.env = env
if not support_multi_env and self.n_envs > 1:
raise ValueError(
"Error: the model does not support multiple envs requires a single vectorized"
" environment.")
