def build_q_models(policy: Policy, obs_space: gym.Space,
action_space: gym.Space,
config: TrainerConfigDict) -> ModelV2:
if not isinstance(action_space, gym.spaces.Discrete):
raise UnsupportedSpaceException(
"Action space {} is not supported for DQN.".format(action_space))
policy.q_model = ModelCatalog.get_model_v2(obs_space=obs_space,
action_space=action_space,
num_outputs=action_space.n,
model_config=config["model"],
framework=config["framework"],
name=Q_SCOPE)
policy.target_q_model = ModelCatalog.get_model_v2(
obs_space=obs_space,
action_space=action_space,
num_outputs=action_space.n,
model_config=config["model"],
framework=config["framework"],
name=Q_TARGET_SCOPE)
policy.q_func_vars = policy.q_model.variables()
policy.target_q_func_vars = policy.target_q_model.variables()
return policy.q_model
def _build_q_models(policy: Policy, obs_space: gym.spaces.Space,
action_space: gym.spaces.Space,
config: TrainerConfigDict) -> ModelV2:
"""Build q_model and target_q_model for Simple Q learning
Note that this function works for both Tensorflow and PyTorch.
Args:
policy (Policy): The Policy, which will use the model for optimization.
obs_space (gym.spaces.Space): The policy's observation space.
action_space (gym.spaces.Space): The policy's action space.
config (TrainerConfigDict):
Returns:
ModelV2: The Model for the Policy to use.
Note: The target q model will not be returned, just assigned to
`policy.target_q_model`.
"""
if not isinstance(action_space, gym.spaces.Discrete):
raise UnsupportedSpaceException(
"Action space {} is not supported for DQN.".format(action_space))
policy.q_model = ModelCatalog.get_model_v2(obs_space=obs_space,
action_space=action_space,
num_outputs=action_space.n,
model_config=config["model"],
framework=config["framework"],
name=Q_SCOPE)
if torch.cuda.is_available():
policy.q_model = policy.q_model.to("cuda")
policy.target_q_model = ModelCatalog.get_model_v2(
obs_space=obs_space,
action_space=action_space,
num_outputs=action_space.n,
model_config=config["model"],
framework=config["framework"],
name=Q_TARGET_SCOPE)
if torch.cuda.is_available():
policy.target_q_model = policy.target_q_model.to("cuda")
policy.q_func_vars = policy.q_model.variables()
policy.target_q_func_vars = policy.target_q_model.variables()
return policy.q_model
