from ray.rllib.algorithms.ppo.ppo import PPOConfig

config = (
    PPOConfig()
    .learners(
        num_learners=0,  # Set this to greater than 1 to allow for DDP style updates.
        num_gpus_per_learner=0,  # Set this to 1 to enable GPU training.
        num_cpus_per_learner=1,
    )
)

config = config.environment("CartPole-v1")
config.build()  # test that the algorithm can be built with the given resources

This features is in alpha. If you migrate to this algorithm, enable the feature by
via `AlgorithmConfig.api_stack(enable_rl_module_and_learner=True, enable_env_runner_and_connector_v2=True)`.

The following algorithms support :py:class:`~ray.rllib.core.learner.learner.Learner` out of the box. Implement
an algorithm with a custom :py:class:`~ray.rllib.core.learner.learner.Learner` to leverage this API for other algorithms.

.. list-table::
   :header-rows: 1
   :widths: 60 60

   * - Algorithm
     - Supported Framework
   * - **PPO**
     - |pytorch| |tensorflow|
   * - **IMPALA**
     - |pytorch| |tensorflow|
   * - **APPO**
     - |pytorch| |tensorflow|

# imports for the examples
import gymnasium as gym
import numpy as np

import ray
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.core.rl_module.rl_module import RLModuleSpec
from ray.rllib.core.learner.learner_group import LearnerGroup

.. tab-item:: Constructing a LearnerGroup


    .. testcode::

        env = gym.make("CartPole-v1")

        # Create an AlgorithmConfig object from which we can build the
        # LearnerGroup.
        config = (
            PPOConfig()
            # Number of Learner workers (Ray actors).
            # Use 0 for no actors, only create a local Learner.
            # Use >=1 to create n DDP-style Learner workers (Ray actors).
            .learners(num_learners=1)
            # Specify the learner's hyperparameters.
            .training(
                use_kl_loss=True,
                kl_coeff=0.01,
                kl_target=0.05,
                clip_param=0.2,
                vf_clip_param=0.2,
                entropy_coeff=0.05,
                vf_loss_coeff=0.5
            )
        )

        # Construct a new LearnerGroup using our config object.
        learner_group = config.build_learner_group(env=env)

.. tab-item:: Constructing a Learner

    .. testcode::

        env = gym.make("CartPole-v1")

        # Create an AlgorithmConfig object from which we can build the
        # Learner.
        config = (
            PPOConfig()
            # Specify the Learner's hyperparameters.
            .training(
                use_kl_loss=True,
                kl_coeff=0.01,
                kl_target=0.05,
                clip_param=0.2,
                vf_clip_param=0.2,
                entropy_coeff=0.05,
                vf_loss_coeff=0.5
            )
        )
        # Construct a new Learner using our config object.
        learner = config.build_learner(env=env)

        # Needs to be called on the learner before calling any functions.
        learner.build()

import time

from ray.rllib.core import DEFAULT_MODULE_ID
from ray.rllib.evaluation.postprocessing import Postprocessing
from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch

DUMMY_BATCH = {
    SampleBatch.OBS: np.array(
        [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]],
        dtype=np.float32,
    ),
    SampleBatch.NEXT_OBS: np.array(
        [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]],
        dtype=np.float32,
    ),
    SampleBatch.ACTIONS: np.array([0, 1, 1]),
    SampleBatch.PREV_ACTIONS: np.array([0, 1, 1]),
    SampleBatch.REWARDS: np.array([1.0, -1.0, 0.5], dtype=np.float32),
    SampleBatch.PREV_REWARDS: np.array([1.0, -1.0, 0.5], dtype=np.float32),
    SampleBatch.TERMINATEDS: np.array([False, False, True]),
    SampleBatch.TRUNCATEDS: np.array([False, False, False]),
    SampleBatch.VF_PREDS: np.array([0.5, 0.6, 0.7], dtype=np.float32),
    SampleBatch.ACTION_DIST_INPUTS: np.array(
        [[-2.0, 0.5], [-3.0, -0.3], [-0.1, 2.5]], dtype=np.float32
    ),
    SampleBatch.ACTION_LOGP: np.array([-0.5, -0.1, -0.2], dtype=np.float32),
    SampleBatch.EPS_ID: np.array([0, 0, 0]),
    SampleBatch.AGENT_INDEX: np.array([0, 0, 0]),
    Postprocessing.ADVANTAGES: np.array([0.1, 0.2, 0.3], dtype=np.float32),
    Postprocessing.VALUE_TARGETS: np.array([0.5, 0.6, 0.7], dtype=np.float32),
}
default_batch = SampleBatch(DUMMY_BATCH)
DUMMY_BATCH = default_batch.as_multi_agent()
# Make sure, we convert the batch to the correct framework (here: torch).
DUMMY_BATCH = learner._convert_batch_type(DUMMY_BATCH)

.. tab-item:: Updating a LearnerGroup

    .. testcode::

        TIMESTEPS = {"num_env_steps_sampled_lifetime": 250}

        # This is a blocking update.
        results = learner_group.update(batch=DUMMY_BATCH, timesteps=TIMESTEPS)

        # This is a non-blocking update. The results are returned in a future
        # call to `update(..., async_update=True)`
        _ = learner_group.update(batch=DUMMY_BATCH, async_update=True, timesteps=TIMESTEPS)

        # Artificially wait for async request to be done to get the results
        # in the next call to
        # `LearnerGroup.update(..., async_update=True)`.
        time.sleep(5)
        results = learner_group.update(
            batch=DUMMY_BATCH, async_update=True, timesteps=TIMESTEPS
        )
        # `results` is a list of n result dicts from various Learner actors.
        assert isinstance(results, list), results
        assert isinstance(results[0], dict), results

    When updating a :py:class:`~ray.rllib.core.learner.learner_group.LearnerGroup` you can perform blocking or async updates on batches of data.
    Async updates are necessary for implementing async algorithms such as APPO/IMPALA.

.. tab-item:: Updating a Learner

    .. testcode::

        # This is a blocking update (given a training batch).
        result = learner.update(batch=DUMMY_BATCH, timesteps=TIMESTEPS)

    When updating a :py:class:`~ray.rllib.core.learner.learner.Learner` you can only perform blocking updates on batches of data.
    You can perform non-gradient based updates before or after the gradient-based ones by overriding
    :py:meth:`~ray.rllib.core.learner.learner.Learner.before_gradient_based_update` and
    :py:meth:`~ray.rllib.core.learner.learner.Learner.after_gradient_based_update`.

.. tab-item:: Getting and Setting State for a LearnerGroup

    .. testcode::

        # Get the LearnerGroup's RLModule weights and optimizer states.
        state = learner_group.get_state()
        learner_group.set_state(state)

        # Only get the RLModule weights.
        weights = learner_group.get_weights()
        learner_group.set_weights(weights)

    Set/get the state dict of all learners through learner_group through
    `LearnerGroup.set_state` or `LearnerGroup.get_state`.
    This includes the neural network weights
    and the optimizer states on each learner. For example an Adam optimizer's state
    has momentum information based on recently computed gradients.
    If you only want to get or set the weights of the RLModules (neural networks) of
    all Learners, you can do so through the LearnerGroup APIs
    `LearnerGroup.get_weights` and `LearnerGroup.set_weights`.

.. tab-item:: Getting and Setting State for a Learner

    .. testcode::

        from ray.rllib.core import COMPONENT_RL_MODULE

        # Get the Learner's RLModule weights and optimizer states.
        state = learner.get_state()
        # Note that `state` is now a dict:
        # {
        #    COMPONENT_RL_MODULE: [RLModule's state],
        #    COMPONENT_OPTIMIZER: [Optimizer states],
        # }
        learner.set_state(state)

        # Only get the RLModule weights (as numpy, not torch/tf).
        rl_module_only_state = learner.get_state(components=COMPONENT_RL_MODULE)
        # Note that `rl_module_only_state` is now a dict:
        # {COMPONENT_RL_MODULE: [RLModule's state]}
        learner.module.set_state(rl_module_only_state)

    You can set and get the entire state of a :py:class:`~ray.rllib.core.learner.learner.Learner`
    using :py:meth:`~ray.rllib.core.learner.learner.Learner.set_state`
    and :py:meth:`~ray.rllib.core.learner.learner.Learner.get_state` .
    For getting only the RLModule's weights (without optimizer states), use
    the `components=COMPONENT_RL_MODULE` arg in :py:meth:`~ray.rllib.core.learner.learner.Learner.get_state`
    (see code above).
    For setting only the RLModule's weights (without touching the optimizer states), use
    :py:meth:`~ray.rllib.core.learner.learner.Learner.get_state` and pass in a dict:
    `{COMPONENT_RL_MODULE: [RLModule's state]}` (see code above).

import tempfile

LEARNER_CKPT_DIR = tempfile.mkdtemp()
LEARNER_GROUP_CKPT_DIR = tempfile.mkdtemp()

.. tab-item:: Checkpointing a LearnerGroup

    .. testcode::

        learner_group.save_to_path(LEARNER_GROUP_CKPT_DIR)
        learner_group.restore_from_path(LEARNER_GROUP_CKPT_DIR)

    Checkpoint the state of all learners in the :py:class:`~ray.rllib.core.learner.learner_group.LearnerGroup`
    through :py:meth:`~ray.rllib.core.learner.learner_group.LearnerGroup.save_to_path` and restore
    the state of a saved :py:class:`~ray.rllib.core.learner.learner_group.LearnerGroup` through :py:meth:`~ray.rllib.core.learner.learner_group.LearnerGroup.restore_from_path`.
    A LearnerGroup's state includes the neural network weights and all optimizer states.
    Note that since the state of all of the :py:class:`~ray.rllib.core.learner.learner.Learner` instances is identical,
    only the states from the first :py:class:`~ray.rllib.core.learner.learner.Learner` are saved.

.. tab-item:: Checkpointing a Learner

    .. testcode::

        learner.save_to_path(LEARNER_CKPT_DIR)
        learner.restore_from_path(LEARNER_CKPT_DIR)

    Checkpoint the state of a :py:class:`~ray.rllib.core.learner.learner.Learner`
    through :py:meth:`~ray.rllib.core.learner.learner.Learner.save_to_path` and restore the state
    of a saved :py:class:`~ray.rllib.core.learner.learner.Learner` through :py:meth:`~ray.rllib.core.learner.learner.Learner.restore_from_path`.
    A Learner's state includes the neural network weights and all optimizer states.

from typing import Any, Dict, DefaultDict

from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
from ray.rllib.core.learner.learner import Learner
from ray.rllib.core.learner.torch.torch_learner import TorchLearner
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override
from ray.rllib.utils.numpy import convert_to_numpy
from ray.rllib.utils.typing import ModuleID, TensorType

class BCTorchLearner(TorchLearner):

    @override(Learner)
    def compute_loss_for_module(
        self,
        *,
        module_id: ModuleID,
        config: AlgorithmConfig = None,
        batch: Dict[str, Any],
        fwd_out: Dict[str, TensorType],
    ) -> TensorType:

        # standard behavior cloning loss
        action_dist_inputs = fwd_out[SampleBatch.ACTION_DIST_INPUTS]
        action_dist_class = self._module[module_id].get_train_action_dist_cls()
        action_dist = action_dist_class.from_logits(action_dist_inputs)
        loss = -torch.mean(action_dist.logp(batch[SampleBatch.ACTIONS]))

        return loss