from ray.rllib.agents.trainer import with_common_config
from ray.rllib.agents.dqn.dqn import GenericOffPolicyTrainer
from ray.rllib.agents.qmix.qmix_policy import QMixTorchPolicy
from ray.rllib.execution.replay_ops import SimpleReplayBuffer, Replay, \
    StoreToReplayBuffer
from ray.rllib.execution.rollout_ops import ParallelRollouts, ConcatBatches
from ray.rllib.execution.train_ops import TrainOneStep, UpdateTargetNetwork
from ray.rllib.execution.metric_ops import StandardMetricsReporting
from ray.rllib.execution.concurrency_ops import Concurrently

# yapf: disable
# __sphinx_doc_begin__
DEFAULT_CONFIG = with_common_config({
    # === QMix ===
    # Mixing network. Either "qmix", "vdn", or None
    "mixer": "qmix",
    # Size of the mixing network embedding
    "mixing_embed_dim": 32,
    # Whether to use Double_Q learning
    "double_q": True,
    # Optimize over complete episodes by default.
    "batch_mode": "complete_episodes",

    # === Exploration Settings ===
    "exploration_config": {
        # The Exploration class to use.
        "type": "EpsilonGreedy",
        # Config for the Exploration class' constructor:
        "initial_epsilon": 1.0,
        "final_epsilon": 0.02,
        "epsilon_timesteps": 10000,  # Timesteps over which to anneal epsilon.

        # For soft_q, use:
        # "exploration_config" = {
        #   "type": "SoftQ"
        #   "temperature": [float, e.g. 1.0]
        # }
    },

    # === Evaluation ===
    # Evaluate with epsilon=0 every `evaluation_interval` training iterations.
    # The evaluation stats will be reported under the "evaluation" metric key.
    # Note that evaluation is currently not parallelized, and that for Ape-X
    # metrics are already only reported for the lowest epsilon workers.
    "evaluation_interval": None,
    # Number of episodes to run per evaluation period.
    "evaluation_num_episodes": 10,
    # Switch to greedy actions in evaluation workers.
    "evaluation_config": {
        "explore": False,
    },

    # Number of env steps to optimize for before returning
    "timesteps_per_iteration": 1000,
    # Update the target network every `target_network_update_freq` steps.
    "target_network_update_freq": 500,

    # === Replay buffer ===
    # Size of the replay buffer in batches (not timesteps!).
    "buffer_size": 1000,

    # === Optimization ===
    # Learning rate for RMSProp optimizer
    "lr": 0.0005,
    # RMSProp alpha
    "optim_alpha": 0.99,
    # RMSProp epsilon
    "optim_eps": 0.00001,
    # If not None, clip gradients during optimization at this value
    "grad_norm_clipping": 10,
    # How many steps of the model to sample before learning starts.
    "learning_starts": 1000,
    # Update the replay buffer with this many samples at once. Note that
    # this setting applies per-worker if num_workers > 1.
    "rollout_fragment_length": 4,
    # Size of a batched sampled from replay buffer for training. Note that
    # if async_updates is set, then each worker returns gradients for a
    # batch of this size.
    "train_batch_size": 32,

    # === Parallelism ===
    # Number of workers for collecting samples with. This only makes sense
    # to increase if your environment is particularly slow to sample, or if
    # you"re using the Async or Ape-X optimizers.
    "num_workers": 0,
    # Whether to use a distribution of epsilons across workers for exploration.
    "per_worker_exploration": False,
    # Whether to compute priorities on workers.
    "worker_side_prioritization": False,
    # Prevent iterations from going lower than this time span
    "min_iter_time_s": 1,

    # === Model ===
    "model": {
        "lstm_cell_size": 64,
        "max_seq_len": 999999,
    },
})
# __sphinx_doc_end__
# yapf: enable


def execution_plan(workers, config):
    rollouts = ParallelRollouts(workers, mode="bulk_sync")
    replay_buffer = SimpleReplayBuffer(config["buffer_size"])

    store_op = rollouts \
        .for_each(StoreToReplayBuffer(local_buffer=replay_buffer))

    train_op = Replay(local_buffer=replay_buffer) \
        .combine(
        ConcatBatches(
            min_batch_size=config["train_batch_size"],
            count_steps_by=config["multiagent"]["count_steps_by"]
        )) \
        .for_each(TrainOneStep(workers)) \
        .for_each(UpdateTargetNetwork(
            workers, config["target_network_update_freq"]))

    merged_op = Concurrently(
        [store_op, train_op], mode="round_robin", output_indexes=[1])

    return StandardMetricsReporting(merged_op, workers, config)


QMixTrainer = GenericOffPolicyTrainer.with_updates(
    name="QMIX",
    default_config=DEFAULT_CONFIG,
    default_policy=QMixTorchPolicy,
    get_policy_class=None,
    execution_plan=execution_plan)