"""A more stable successor to TD3.

By default, this uses a near-identical configuration to that reported in the
TD3 paper.
"""
from ray.rllib.algorithms.ddpg.ddpg import DDPG, DDPGConfig
from ray.rllib.utils.annotations import override
from ray.rllib.utils.deprecation import Deprecated
from ray.rllib.utils.typing import AlgorithmConfigDict
from ray.rllib.utils.deprecation import DEPRECATED_VALUE


class TD3Config(DDPGConfig):
    """Defines a configuration class from which a TD3 Algorithm can be built.

    Example:
        >>> from ray.rllib.algorithms.ddpg.td3 import TD3Config
        >>> config = TD3Config().training(lr=0.01).resources(num_gpus=1)
        >>> print(config.to_dict())
        >>> # Build a Algorithm object from the config and run one training iteration.
        >>> trainer = config.build(env="Pendulum-v1")
        >>> trainer.train()

    Example:
        >>> from ray.rllib.algorithms.ddpg.td3 import TD3Config
        >>> from ray import tune
        >>> config = TD3Config()
        >>> # Print out some default values.
        >>> print(config.lr)
            0.0004
        >>> # Update the config object.
        >>> config.training(lr=tune.grid_search([0.001, 0.0001]))
        >>> # Set the config object's env.
        >>> config.environment(env="Pendulum-v1")
        >>> # Use to_dict() to get the old-style python config dict
        >>> # when running with tune.
        >>> tune.run(
        ...     "TD3",
        ...     stop={"episode_reward_mean": 200},
        ...     config=config.to_dict(),
        ... )
    """

    def __init__(self, algo_class=None):
        """Initializes a TD3Config instance."""
        super().__init__(algo_class=algo_class or TD3)

        # fmt: off
        # __sphinx_doc_begin__

        # Override some of DDPG/SimpleQ/Algorithm's default values with TD3-specific
        # values.

        # .training()

        # largest changes: twin Q functions, delayed policy updates, target
        # smoothing, no l2-regularization.
        self.twin_q = True
        self.policy_delay = 2
        self.smooth_target_policy = True,
        self.l2_reg = 0.0
        # Different tau (affecting target network update).
        self.tau = 5e-3
        # Different batch size.
        self.train_batch_size = 100
        # No prioritized replay by default (we may want to change this at some
        # point).
        self.replay_buffer_config = {
            "type": "MultiAgentReplayBuffer",
            # Specify prioritized replay by supplying a buffer type that supports
            # prioritization, for example: MultiAgentPrioritizedReplayBuffer.
            "prioritized_replay": DEPRECATED_VALUE,
            "capacity": 1000000,
            "learning_starts": 10000,
            "worker_side_prioritization": False,
        }

        # .exploration()
        # TD3 uses Gaussian Noise by default.
        self.exploration_config = {
            # TD3 uses simple Gaussian noise on top of deterministic NN-output
            # actions (after a possible pure random phase of n timesteps).
            "type": "GaussianNoise",
            # For how many timesteps should we return completely random
            # actions, before we start adding (scaled) noise?
            "random_timesteps": 10000,
            # Gaussian stddev of action noise for exploration.
            "stddev": 0.1,
            # Scaling settings by which the Gaussian noise is scaled before
            # being added to the actions. NOTE: The scale timesteps start only
            # after(!) any random steps have been finished.
            # By default, do not anneal over time (fixed 1.0).
            "initial_scale": 1.0,
            "final_scale": 1.0,
            "scale_timesteps": 1,
        }
        # __sphinx_doc_end__
        # fmt: on


class TD3(DDPG):
    @classmethod
    @override(DDPG)
    def get_default_config(cls) -> AlgorithmConfigDict:
        return TD3Config().to_dict()


# Deprecated: Use ray.rllib.algorithms.ddpg..td3.TD3Config instead!
class _deprecated_default_config(dict):
    def __init__(self):
        super().__init__(TD3Config().to_dict())

    @Deprecated(
        old="ray.rllib.algorithms.ddpg.td3::TD3_DEFAULT_CONFIG",
        new="ray.rllib.algorithms.td3.td3::TD3Config(...)",
        error=False,
    )
    def __getitem__(self, item):
        return super().__getitem__(item)


TD3_DEFAULT_CONFIG = _deprecated_default_config()

"""A more stable successor to TD3.

By default, this uses a near-identical configuration to that reported in the
TD3 paper.
"""
from ray.rllib.algorithms.ddpg.ddpg import DDPG, DDPGConfig
from ray.rllib.utils.annotations import override
from ray.rllib.utils.deprecation import Deprecated
from ray.rllib.utils.typing import AlgorithmConfigDict
from ray.rllib.utils.deprecation import DEPRECATED_VALUE


class TD3Config(DDPGConfig):
    """Defines a configuration class from which a TD3 Algorithm can be built.

    Example:
        >>> from ray.rllib.algorithms.ddpg.td3 import TD3Config
        >>> config = TD3Config().training(lr=0.01).resources(num_gpus=1)
        >>> print(config.to_dict())
        >>> # Build a Algorithm object from the config and run one training iteration.
        >>> trainer = config.build(env="Pendulum-v1")
        >>> trainer.train()

    Example:
        >>> from ray.rllib.algorithms.ddpg.td3 import TD3Config
        >>> from ray import tune
        >>> config = TD3Config()
        >>> # Print out some default values.
        >>> print(config.lr)
            0.0004
        >>> # Update the config object.
        >>> config.training(lr=tune.grid_search([0.001, 0.0001]))
        >>> # Set the config object's env.
        >>> config.environment(env="Pendulum-v1")
        >>> # Use to_dict() to get the old-style python config dict
        >>> # when running with tune.
        >>> tune.run(
        ...     "TD3",
        ...     stop={"episode_reward_mean": 200},
        ...     config=config.to_dict(),
        ... )
    """

    def __init__(self, algo_class=None):
        """Initializes a TD3Config instance."""
        super().__init__(algo_class=algo_class or TD3)

        # fmt: off
        # __sphinx_doc_begin__

        # Override some of DDPG/SimpleQ/Algorithm's default values with TD3-specific
        # values.

        # .training()

        # largest changes: twin Q functions, delayed policy updates, target
        # smoothing, no l2-regularization.
        self.twin_q = True
        self.policy_delay = 2
        self.smooth_target_policy = True,
        self.l2_reg = 0.0
        # Different tau (affecting target network update).
        self.tau = 5e-3
        # Different batch size.
        self.train_batch_size = 100
        # No prioritized replay by default (we may want to change this at some
        # point).
        self.replay_buffer_config = {
            "type": "MultiAgentReplayBuffer",
            # Specify prioritized replay by supplying a buffer type that supports
            # prioritization, for example: MultiAgentPrioritizedReplayBuffer.
            "prioritized_replay": DEPRECATED_VALUE,
            "capacity": 1000000,
            "learning_starts": 10000,
            "worker_side_prioritization": False,
        }

        # .exploration()
        # TD3 uses Gaussian Noise by default.
        self.exploration_config = {
            # TD3 uses simple Gaussian noise on top of deterministic NN-output
            # actions (after a possible pure random phase of n timesteps).
            "type": "GaussianNoise",
            # For how many timesteps should we return completely random
            # actions, before we start adding (scaled) noise?
            "random_timesteps": 10000,
            # Gaussian stddev of action noise for exploration.
            "stddev": 0.1,
            # Scaling settings by which the Gaussian noise is scaled before
            # being added to the actions. NOTE: The scale timesteps start only
            # after(!) any random steps have been finished.
            # By default, do not anneal over time (fixed 1.0).
            "initial_scale": 1.0,
            "final_scale": 1.0,
            "scale_timesteps": 1,
        }
        # __sphinx_doc_end__
        # fmt: on


class TD3(DDPG):
    @classmethod
    @override(DDPG)
    def get_default_config(cls) -> AlgorithmConfigDict:
        return TD3Config().to_dict()


# Deprecated: Use ray.rllib.algorithms.ddpg..td3.TD3Config instead!
class _deprecated_default_config(dict):
    def __init__(self):
        super().__init__(TD3Config().to_dict())

    @Deprecated(
        old="ray.rllib.algorithms.ddpg.td3::TD3_DEFAULT_CONFIG",
        new="ray.rllib.algorithms.td3.td3::TD3Config(...)",
        error=False,
    )
    def __getitem__(self, item):
        return super().__getitem__(item)


TD3_DEFAULT_CONFIG = _deprecated_default_config()

A more stable successor to TD3.  By default, this uses a near-identical configuration to that reported in the TD3 paper. Defines a configuration class from which a TD3 Algorithm can be built.      Example:         >>> from ray.rllib.algorithms.ddpg.td3 import TD3Config         >>> config = TD3Config().training(lr=0.01).resources(num_gpus=1)         >>> print(config.to_dict())         >>> # Build a Algorithm object from the config and run one training iteration.         >>> trainer = config.build(env="Pendulum-v1")         >>> trainer.train()      Example:         >>> from ray.rllib.algorithms.ddpg.td3 import TD3Config         >>> from ray import tune         >>> config = TD3Config()         >>> # Print out some default values.         >>> print(config.lr)             0.0004         >>> # Update the config object.         >>> config.training(lr=tune.grid_search([0.001, 0.0001]))         >>> # Set the config object's env.         >>> config.environment(env="Pendulum-v1")         >>> # Use to_dict() to get the old-style python config dict         >>> # when running with tune.         >>> tune.run(         ...     "TD3",         ...     stop={"episode_reward_mean": 200},         ...     config=config.to_dict(),         ... ) Initializes a TD3Config instance. # fmt: off # __sphinx_doc_begin__ # Override some of DDPG/SimpleQ/Algorithm's default values with TD3-specific # values. # .training() # largest changes: twin Q functions, delayed policy updates, target # smoothing, no l2-regularization. # Different tau (affecting target network update). # Different batch size. # No prioritized replay by default (we may want to change this at some # point). # Specify prioritized replay by supplying a buffer type that supports # prioritization, for example: MultiAgentPrioritizedReplayBuffer. # .exploration() # TD3 uses Gaussian Noise by default. # TD3 uses simple Gaussian noise on top of deterministic NN-output # actions (after a possible pure random phase of n timesteps). # For how many timesteps should we return completely random # actions, before we start adding (scaled) noise? # Gaussian stddev of action noise for exploration. # Scaling settings by which the Gaussian noise is scaled before # being added to the actions. NOTE: The scale timesteps start only # after(!) any random steps have been finished. # By default, do not anneal over time (fixed 1.0). # __sphinx_doc_end__ # fmt: on # Deprecated: Use ray.rllib.algorithms.ddpg..td3.TD3Config instead!