ray/rllib/agents/marwil/marwil.py

from ray.rllib.agents.trainer import with_common_config
from ray.rllib.agents.trainer_template import build_trainer
from ray.rllib.agents.marwil.marwil_tf_policy import MARWILTFPolicy
from ray.rllib.optimizers import SyncBatchReplayOptimizer

# yapf: disable
# __sphinx_doc_begin__
DEFAULT_CONFIG = with_common_config({
    # You should override this to point to an offline dataset (see agent.py).
    "input": "sampler",
    # Use importance sampling estimators for reward
    "input_evaluation": ["is", "wis"],

    # Scaling of advantages in exponential terms
    # When beta is 0, MARWIL is reduced to imitation learning
    "beta": 1.0,
    # Balancing value estimation loss and policy optimization loss
    "vf_coeff": 1.0,
    # Whether to calculate cumulative rewards
    "postprocess_inputs": True,
    # Whether to rollout "complete_episodes" or "truncate_episodes"
    "batch_mode": "complete_episodes",
    # Learning rate for adam optimizer
    "lr": 1e-4,
    # Number of timesteps collected for each SGD round
    "train_batch_size": 2000,
    # Number of steps max to keep in the batch replay buffer
    "replay_buffer_size": 100000,
    # Number of steps to read before learning starts
    "learning_starts": 0,
    # === Parallelism ===
    "num_workers": 0,
    # Use PyTorch as framework?
    "use_pytorch": False
})
# __sphinx_doc_end__
# yapf: enable


def make_optimizer(workers, config):
    return SyncBatchReplayOptimizer(
        workers,
        learning_starts=config["learning_starts"],
        buffer_size=config["replay_buffer_size"],
        train_batch_size=config["train_batch_size"],
    )


def get_policy_class(config):
    if config.get("use_pytorch") is True:
        from ray.rllib.agents.marwil.marwil_torch_policy import \
            MARWILTorchPolicy
        return MARWILTorchPolicy
    else:
        return MARWILTFPolicy


MARWILTrainer = build_trainer(
    name="MARWIL",
    default_config=DEFAULT_CONFIG,
    default_policy=MARWILTFPolicy,
    get_policy_class=get_policy_class,
    make_policy_optimizer=make_optimizer)