"""The two-step game from QMIX: https://arxiv.org/pdf/1803.11485.pdf

Configurations you can try:
    - normal policy gradients (PG)
    - contrib/MADDPG
    - QMIX
    - APEX_QMIX

See also: centralized_critic.py for centralized critic PPO on this game.
"""

import argparse
from gym.spaces import Tuple, MultiDiscrete, Dict, Discrete

import ray
from ray import tune
from ray.tune import register_env, grid_search
from ray.rllib.env.multi_agent_env import ENV_STATE
from ray.rllib.examples.env.two_step_game import TwoStepGame

parser = argparse.ArgumentParser()
parser.add_argument("--stop", type=int, default=50000)
parser.add_argument("--run", type=str, default="PG")
parser.add_argument("--num-cpus", type=int, default=0)

if __name__ == "__main__":
    args = parser.parse_args()

    grouping = {
        "group_1": [0, 1],
    }
    obs_space = Tuple([
        Dict({
            "obs": MultiDiscrete([2, 2, 2, 3]),
            ENV_STATE: MultiDiscrete([2, 2, 2])
        }),
        Dict({
            "obs": MultiDiscrete([2, 2, 2, 3]),
            ENV_STATE: MultiDiscrete([2, 2, 2])
        }),
    ])
    act_space = Tuple([
        TwoStepGame.action_space,
        TwoStepGame.action_space,
    ])
    register_env(
        "grouped_twostep",
        lambda config: TwoStepGame(config).with_agent_groups(
            grouping, obs_space=obs_space, act_space=act_space))

    if args.run == "contrib/MADDPG":
        obs_space_dict = {
            "agent_1": Discrete(6),
            "agent_2": Discrete(6),
        }
        act_space_dict = {
            "agent_1": TwoStepGame.action_space,
            "agent_2": TwoStepGame.action_space,
        }
        config = {
            "learning_starts": 100,
            "env_config": {
                "actions_are_logits": True,
            },
            "multiagent": {
                "policies": {
                    "pol1": (None, Discrete(6), TwoStepGame.action_space, {
                        "agent_id": 0,
                    }),
                    "pol2": (None, Discrete(6), TwoStepGame.action_space, {
                        "agent_id": 1,
                    }),
                },
                "policy_mapping_fn": lambda x: "pol1" if x == 0 else "pol2",
            },
        }
        group = False
    elif args.run == "QMIX":
        config = {
            "rollout_fragment_length": 4,
            "train_batch_size": 32,
            "exploration_fraction": .4,
            "exploration_final_eps": 0.0,
            "num_workers": 0,
            "mixer": grid_search([None, "qmix", "vdn"]),
            "env_config": {
                "separate_state_space": True,
                "one_hot_state_encoding": True
            },
        }
        group = True
    elif args.run == "APEX_QMIX":
        config = {
            "num_gpus": 0,
            "num_workers": 2,
            "optimizer": {
                "num_replay_buffer_shards": 1,
            },
            "min_iter_time_s": 3,
            "buffer_size": 1000,
            "learning_starts": 1000,
            "train_batch_size": 128,
            "rollout_fragment_length": 32,
            "target_network_update_freq": 500,
            "timesteps_per_iteration": 1000,
            "env_config": {
                "separate_state_space": True,
                "one_hot_state_encoding": True
            },
        }
        group = True
    else:
        config = {}
        group = False

    ray.init(num_cpus=args.num_cpus or None)
    tune.run(
        args.run,
        stop={
            "timesteps_total": args.stop,
        },
        config=dict(config, **{
            "env": "grouped_twostep" if group else TwoStepGame,
        }),
    )