def build_r2d2_model(
policy: Policy, obs_space: gym.spaces.Space,
action_space: gym.spaces.Space,
config: TrainerConfigDict) -> Tuple[ModelV2, ActionDistribution]:
"""Build q_model and target_model for DQN
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:
q_model
Note: The target q model will not be returned, just assigned to
`policy.target_model`.
"""
# Create the policy's models.
model = build_q_model(policy, obs_space, action_space, config)
# Assert correct model type.
assert model.get_initial_state() != [], \
"R2D2 requires its model to be a recurrent one! Try using " \
"`model.use_lstm` or `model.use_attention` in your config " \
"to auto-wrap your model with an LSTM- or attention net."
return model
def build_r2d2_model(policy: Policy, obs_space: gym.spaces.Space,
action_space: gym.spaces.Space, config: TrainerConfigDict
) -> Tuple[ModelV2, ActionDistribution]:
"""Build q_model and target_model for DQN
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:
q_model
Note: The target q model will not be returned, just assigned to
`policy.target_model`.
"""
# Create the policy's models.
model = build_q_model(policy, obs_space, action_space, config)
# Assert correct model type by checking the init state to be present.
# For attention nets: These don't necessarily publish their init state via
# Model.get_initial_state, but may only use the trajectory view API
# (view_requirements).
assert (model.get_initial_state() != [] or
model.view_requirements.get("state_in_0") is not None), \
"R2D2 requires its model to be a recurrent one! Try using " \
"`model.use_lstm` or `model.use_attention` in your config " \
"to auto-wrap your model with an LSTM- or attention net."
return model
