# Copyright (c) 2022-2023, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

from __future__ import annotations

from dataclasses import MISSING

from omni.isaac.orbit.utils import configclass


@configclass
class RslRlPpoActorCriticCfg:
    """Configuration for the PPO actor-critic networks."""

    class_name: str = "ActorCritic"
    """The policy class name. Default is ActorCritic."""

    init_noise_std: float = MISSING
    """The initial noise standard deviation for the policy."""

    actor_hidden_dims: list[int] = MISSING
    """The hidden dimensions of the actor network."""

    critic_hidden_dims: list[int] = MISSING
    """The hidden dimensions of the critic network."""

    activation: str = MISSING
    """The activation function for the actor and critic networks."""


@configclass
class RslRlPpoAlgorithmCfg:
    """Configuration for the PPO algorithm."""

    class_name: str = "PPO"
    """The algorithm class name. Default is PPO."""

    value_loss_coef: float = MISSING
    """The coefficient for the value loss."""

    use_clipped_value_loss: bool = MISSING
    """Whether to use clipped value loss."""

    clip_param: float = MISSING
    """The clipping parameter for the policy."""

    entropy_coef: float = MISSING
    """The coefficient for the entropy loss."""

    num_learning_epochs: int = MISSING
    """The number of learning epochs per update."""

    num_mini_batches: int = MISSING
    """The number of mini-batches per update."""

    learning_rate: float = MISSING
    """The learning rate for the policy."""

    schedule: str = MISSING
    """The learning rate schedule."""

    gamma: float = MISSING
    """The discount factor."""

    lam: float = MISSING
    """The lambda parameter for Generalized Advantage Estimation (GAE)."""

    desired_kl: float = MISSING
    """The desired KL divergence."""

    max_grad_norm: float = MISSING
    """The maximum gradient norm."""


@configclass
class RslRlOnPolicyRunnerCfg:
    """Configuration of the runner for on-policy algorithms."""

    seed: int = 42
    """The seed for the experiment. Default is 42."""

    device: str = "cuda"
    """The device for the rl-agent. Default is cuda."""

    num_steps_per_env: int = MISSING
    """The number of steps per environment per update."""

    max_iterations: int = MISSING
    """The maximum number of iterations."""

    empirical_normalization: bool = MISSING
    """Whether to use empirical normalization."""

    save_interval: int = MISSING
    """The number of iterations between saves."""

    experiment_name: str = MISSING
    """The experiment name."""

    run_name: str = ""
    """The run name. Default is empty string."""

    resume: bool = False
    """Whether to resume. Default is False."""

    load_run: str = ".*"
    """The run directory to load. Default is ".*" (all).

    If regex expression, the latest (alphabetical order) matching run will be loaded.
    """

    load_checkpoint: str = "model_.*.pt"
    """The checkpoint file to load. Default is ``"model_.*.pt"`` (all).

    If regex expression, the latest (alphabetical order) matching file will be loaded.
    """

    policy: RslRlPpoActorCriticCfg = MISSING
    """The policy configuration."""

    algorithm: RslRlPpoAlgorithmCfg = MISSING
    """The algorithm configuration."""
