from torch import nn

from ray.rllib.algorithms.sac import SACConfig
from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
from ray.rllib.examples.envs.classes.multi_agent import MultiAgentPendulum
from ray.rllib.examples.utils import (
    add_rllib_example_script_args,
    run_rllib_example_script_experiment,
)
from ray.rllib.utils.metrics import (
    ENV_RUNNER_RESULTS,
    EPISODE_RETURN_MEAN,
    NUM_ENV_STEPS_SAMPLED_LIFETIME,
)
from ray.tune.registry import register_env

parser = add_rllib_example_script_args(
    default_timesteps=500000,
)
parser.set_defaults(
    num_agents=2,
)
# Use `parser` to add your own custom command line options to this script
# and (if needed) use their values to set up `config` below.
args = parser.parse_args()

register_env("multi_agent_pendulum", lambda cfg: MultiAgentPendulum(config=cfg))

config = (
    SACConfig()
    .environment("multi_agent_pendulum", env_config={"num_agents": args.num_agents})
    .training(
        initial_alpha=1.001,
        # Use a smaller learning rate for the policy.
        actor_lr=2e-4 * (args.num_learners or 1) ** 0.5,
        critic_lr=8e-4 * (args.num_learners or 1) ** 0.5,
        alpha_lr=9e-4 * (args.num_learners or 1) ** 0.5,
        lr=None,
        target_entropy="auto",
        n_step=(2, 5),
        tau=0.005,
        train_batch_size_per_learner=256,
        target_network_update_freq=1,
        replay_buffer_config={
            "type": "MultiAgentPrioritizedEpisodeReplayBuffer",
            "capacity": 100000,
            "alpha": 1.0,
            "beta": 0.0,
        },
        num_steps_sampled_before_learning_starts=256,
    )
    .rl_module(
        model_config=DefaultModelConfig(
            fcnet_hiddens=[256, 256],
            fcnet_activation="relu",
            fcnet_kernel_initializer=nn.init.xavier_uniform_,
            head_fcnet_hiddens=[],
            head_fcnet_activation=None,
            head_fcnet_kernel_initializer=nn.init.orthogonal_,
            head_fcnet_kernel_initializer_kwargs={"gain": 0.01},
            fusionnet_hiddens=[256, 256, 256],
            fusionnet_activation="relu",
        ),
    )
    .reporting(
        metrics_num_episodes_for_smoothing=5,
    )
)

if args.num_agents > 0:
    config.multi_agent(
        policy_mapping_fn=lambda aid, *arg, **kw: f"p{aid}",
        policies={f"p{i}" for i in range(args.num_agents)},
    )

stop = {
    NUM_ENV_STEPS_SAMPLED_LIFETIME: args.stop_timesteps,
    # `episode_return_mean` is the sum of all agents/policies' returns.
    f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -450.0 * args.num_agents,
}

if __name__ == "__main__":
    assert (
        args.num_agents > 0
    ), "The `--num-agents` arg must be > 0 for this script to work."

    run_rllib_example_script_experiment(config, args, stop=stop)