awac.py

import os
import random
import uuid
from copy import deepcopy
from dataclasses import asdict, dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import d4rl
import gym
import numpy as np
import pyrallis
import torch
import torch.nn as nn
import torch.nn.functional
import wandb
from tqdm import trange

TensorBatch = List[torch.Tensor]


@dataclass
class TrainConfig:
    # wandb project name
    project: str = "CORL"
    # wandb group name
    group: str = "AWAC-D4RL"
    # wandb run name
    name: str = "AWAC"
    # training dataset and evaluation environment
    env_name: str = "halfcheetah-medium-expert-v2"
    # actor and critic hidden dim
    hidden_dim: int = 256
    # actor and critic learning rate
    learning_rate: float = 3e-4
    # discount factor
    gamma: float = 0.99
    # coefficient for the target critic Polyak's update
    tau: float = 5e-3
    # awac actor loss temperature, controlling balance
    # between behaviour cloning and Q-value maximization
    awac_lambda: float = 1.0
    # total number of gradient updated during training
    num_train_ops: int = 1_000_000
    # training batch size
    batch_size: int = 256
    # maximum size of the replay buffer
    buffer_size: int = 2_000_000
    # whether to normalize reward (like in IQL)
    normalize_reward: bool = False
    # evaluation frequency, will evaluate every eval_frequency
    # training steps
    eval_frequency: int = 1000
    # number of episodes to run during evaluation
    n_test_episodes: int = 10
    # path for checkpoints saving, optional
    checkpoints_path: Optional[str] = None
    # configure PyTorch to use deterministic algorithms instead
    # of nondeterministic ones
    deterministic_torch: bool = False
    # training random seed
    seed: int = 42
    # evaluation random seed
    test_seed: int = 69
    # training device
    device: str = "cuda"

    def __post_init__(self):
        self.name = f"{self.name}-{self.env_name}-{str(uuid.uuid4())[:8]}"
        if self.checkpoints_path is not None:
            self.checkpoints_path = os.path.join(self.checkpoints_path, self.name)


class ReplayBuffer:
    def __init__(
        self,
        state_dim: int,
        action_dim: int,
        buffer_size: int,
        device: str = "cpu",
    ):
        self._buffer_size = buffer_size
        self._pointer = 0
        self._size = 0

        self._states = torch.zeros(
            (buffer_size, state_dim), dtype=torch.float32, device=device
        )
        self._actions = torch.zeros(
            (buffer_size, action_dim), dtype=torch.float32, device=device
        )
        self._rewards = torch.zeros((buffer_size, 1), dtype=torch.float32, device=device)
        self._next_states = torch.zeros(
            (buffer_size, state_dim), dtype=torch.float32, device=device
        )
        self._dones = torch.zeros((buffer_size, 1), dtype=torch.float32, device=device)
        self._device = device

    def _to_tensor(self, data: np.ndarray) -> torch.Tensor:
        return torch.tensor(data, dtype=torch.float32, device=self._device)

    def load_d4rl_dataset(self, data: Dict[str, np.ndarray]):
        if self._size != 0:
            raise ValueError("Trying to load data into non-empty replay buffer")
        n_transitions = data["observations"].shape[0]
        if n_transitions > self._buffer_size:
            raise ValueError(
                "Replay buffer is smaller than the dataset you are trying to load!"
            )
        self._states[:n_transitions] = self._to_tensor(data["observations"])
        self._actions[:n_transitions] = self._to_tensor(data["actions"])
        self._rewards[:n_transitions] = self._to_tensor(data["rewards"][..., None])
        self._next_states[:n_transitions] = self._to_tensor(data["next_observations"])
        self._dones[:n_transitions] = self._to_tensor(data["terminals"][..., None])
        self._size += n_transitions
        self._pointer = min(self._size, n_transitions)

        print(f"Dataset size: {n_transitions}")

    def sample(self, batch_size: int) -> TensorBatch:
        indices = np.random.randint(0, min(self._size, self._pointer), size=batch_size)
        states = self._states[indices]
        actions = self._actions[indices]
        rewards = self._rewards[indices]
        next_states = self._next_states[indices]
        dones = self._dones[indices]
        return [states, actions, rewards, next_states, dones]

    def add_transition(self):
        # Use this method to add new data into the replay buffer during fine-tuning.
        # I left it unimplemented since now we do not do fine-tuning.
        raise NotImplementedError


class Actor(nn.Module):
    def __init__(
        self,
        state_dim: int,
        action_dim: int,
        hidden_dim: int,
        min_log_std: float = -20.0,
        max_log_std: float = 2.0,
        min_action: float = -1.0,
        max_action: float = 1.0,
    ):
        super().__init__()
        self._mlp = nn.Sequential(
            nn.Linear(state_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, action_dim),
        )
        self._log_std = nn.Parameter(torch.zeros(action_dim, dtype=torch.float32))
        self._min_log_std = min_log_std
        self._max_log_std = max_log_std
        self._min_action = min_action
        self._max_action = max_action

    def _get_policy(self, state: torch.Tensor) -> torch.distributions.Distribution:
        mean = self._mlp(state)
        log_std = self._log_std.clamp(self._min_log_std, self._max_log_std)
        policy = torch.distributions.Normal(mean, log_std.exp())
        return policy

    def log_prob(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
        policy = self._get_policy(state)
        log_prob = policy.log_prob(action).sum(-1, keepdim=True)
        return log_prob

    def forward(self, state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        policy = self._get_policy(state)
        action = policy.rsample()
        action.clamp_(self._min_action, self._max_action)
        log_prob = policy.log_prob(action).sum(-1, keepdim=True)
        return action, log_prob

    def act(self, state: np.ndarray, device: str) -> np.ndarray:
        state_t = torch.tensor(state[None], dtype=torch.float32, device=device)
        policy = self._get_policy(state_t)
        if self._mlp.training:
            action_t = policy.sample()
        else:
            action_t = policy.mean
        action = action_t[0].cpu().numpy()
        return action


class Critic(nn.Module):
    def __init__(
        self,
        state_dim: int,
        action_dim: int,
        hidden_dim: int,
    ):
        super().__init__()
        self._mlp = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1),
        )

    def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:
        q_value = self._mlp(torch.cat([state, action], dim=-1))
        return q_value


def soft_update(target: nn.Module, source: nn.Module, tau: float):
    for target_param, source_param in zip(target.parameters(), source.parameters()):
        target_param.data.copy_((1 - tau) * target_param.data + tau * source_param.data)


class AdvantageWeightedActorCritic:
    def __init__(
        self,
        actor: nn.Module,
        actor_optimizer: torch.optim.Optimizer,
        critic_1: nn.Module,
        critic_1_optimizer: torch.optim.Optimizer,
        critic_2: nn.Module,
        critic_2_optimizer: torch.optim.Optimizer,
        gamma: float = 0.99,
        tau: float = 5e-3,  # parameter for the soft target update,
        awac_lambda: float = 1.0,
        exp_adv_max: float = 100.0,
    ):
        self._actor = actor
        self._actor_optimizer = actor_optimizer

        self._critic_1 = critic_1
        self._critic_1_optimizer = critic_1_optimizer
        self._target_critic_1 = deepcopy(critic_1)

        self._critic_2 = critic_2
        self._critic_2_optimizer = critic_2_optimizer
        self._target_critic_2 = deepcopy(critic_2)

        self._gamma = gamma
        self._tau = tau
        self._awac_lambda = awac_lambda
        self._exp_adv_max = exp_adv_max

    def _actor_loss(self, states, actions):
        with torch.no_grad():
            pi_action, _ = self._actor(states)
            v = torch.min(
                self._critic_1(states, pi_action), self._critic_2(states, pi_action)
            )

            q = torch.min(
                self._critic_1(states, actions), self._critic_2(states, actions)
            )
            adv = q - v
            weights = torch.clamp_max(
                torch.exp(adv / self._awac_lambda), self._exp_adv_max
            )

        action_log_prob = self._actor.log_prob(states, actions)
        loss = (-action_log_prob * weights).mean()
        return loss

    def _critic_loss(self, states, actions, rewards, dones, next_states):
        with torch.no_grad():
            next_actions, _ = self._actor(next_states)

            q_next = torch.min(
                self._target_critic_1(next_states, next_actions),
                self._target_critic_2(next_states, next_actions),
            )
            q_target = rewards + self._gamma * (1.0 - dones) * q_next

        q1 = self._critic_1(states, actions)
        q2 = self._critic_2(states, actions)

        q1_loss = nn.functional.mse_loss(q1, q_target)
        q2_loss = nn.functional.mse_loss(q2, q_target)
        loss = q1_loss + q2_loss
        return loss

    def _update_critic(self, states, actions, rewards, dones, next_states):
        loss = self._critic_loss(states, actions, rewards, dones, next_states)
        self._critic_1_optimizer.zero_grad()
        self._critic_2_optimizer.zero_grad()
        loss.backward()
        self._critic_1_optimizer.step()
        self._critic_2_optimizer.step()
        return loss.item()

    def _update_actor(self, states, actions):
        loss = self._actor_loss(states, actions)
        self._actor_optimizer.zero_grad()
        loss.backward()
        self._actor_optimizer.step()
        return loss.item()

    def update(self, batch: TensorBatch) -> Dict[str, float]:
        states, actions, rewards, next_states, dones = batch
        critic_loss = self._update_critic(states, actions, rewards, dones, next_states)
        actor_loss = self._update_actor(states, actions)

        soft_update(self._target_critic_1, self._critic_1, self._tau)
        soft_update(self._target_critic_2, self._critic_2, self._tau)

        result = {"critic_loss": critic_loss, "actor_loss": actor_loss}
        return result

    def state_dict(self) -> Dict[str, Any]:
        return {
            "actor": self._actor.state_dict(),
            "critic_1": self._critic_1.state_dict(),
            "critic_2": self._critic_2.state_dict(),
        }

    def load_state_dict(self, state_dict: Dict[str, Any]):
        self._actor.load_state_dict(state_dict["actor"])
        self._critic_1.load_state_dict(state_dict["critic_1"])
        self._critic_2.load_state_dict(state_dict["critic_2"])


def set_seed(
    seed: int, env: Optional[gym.Env] = None, deterministic_torch: bool = False
):
    if env is not None:
        env.seed(seed)
        env.action_space.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.manual_seed(seed)
    torch.use_deterministic_algorithms(deterministic_torch)


def compute_mean_std(states: np.ndarray, eps: float) -> Tuple[np.ndarray, np.ndarray]:
    mean = states.mean(0)
    std = states.std(0) + eps
    return mean, std


def normalize_states(states: np.ndarray, mean: np.ndarray, std: np.ndarray):
    return (states - mean) / std


def wrap_env(
    env: gym.Env,
    state_mean: Union[np.ndarray, float] = 0.0,
    state_std: Union[np.ndarray, float] = 1.0,
) -> gym.Env:
    def normalize_state(state):
        return (state - state_mean) / state_std

    env = gym.wrappers.TransformObservation(env, normalize_state)
    return env


@torch.no_grad()
def eval_actor(
    env: gym.Env, actor: Actor, device: str, n_episodes: int, seed: int
) -> np.ndarray:
    env.seed(seed)
    actor.eval()
    episode_rewards = []
    for _ in range(n_episodes):
        state, done = env.reset(), False
        episode_reward = 0.0
        while not done:
            action = actor.act(state, device)
            state, reward, done, _ = env.step(action)
            episode_reward += reward
        episode_rewards.append(episode_reward)

    actor.train()
    return np.asarray(episode_rewards)


def return_reward_range(dataset, max_episode_steps):
    returns, lengths = [], []
    ep_ret, ep_len = 0.0, 0
    for r, d in zip(dataset["rewards"], dataset["terminals"]):
        ep_ret += float(r)
        ep_len += 1
        if d or ep_len == max_episode_steps:
            returns.append(ep_ret)
            lengths.append(ep_len)
            ep_ret, ep_len = 0.0, 0
    lengths.append(ep_len)  # but still keep track of number of steps
    assert sum(lengths) == len(dataset["rewards"])
    return min(returns), max(returns)


def modify_reward(dataset, env_name, max_episode_steps=1000):
    if any(s in env_name for s in ("halfcheetah", "hopper", "walker2d")):
        min_ret, max_ret = return_reward_range(dataset, max_episode_steps)
        dataset["rewards"] /= max_ret - min_ret
        dataset["rewards"] *= max_episode_steps
    elif "antmaze" in env_name:
        dataset["rewards"] -= 1.0


def wandb_init(config: dict) -> None:
    wandb.init(
        config=config,
        project=config["project"],
        group=config["group"],
        name=config["name"],
        id=str(uuid.uuid4()),
    )
    wandb.run.save()


@pyrallis.wrap()
def train(config: TrainConfig):
    env = gym.make(config.env_name)
    set_seed(config.seed, env, deterministic_torch=config.deterministic_torch)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]
    dataset = d4rl.qlearning_dataset(env)

    if config.normalize_reward:
        modify_reward(dataset, config.env_name)

    state_mean, state_std = compute_mean_std(dataset["observations"], eps=1e-3)
    dataset["observations"] = normalize_states(
        dataset["observations"], state_mean, state_std
    )
    dataset["next_observations"] = normalize_states(
        dataset["next_observations"], state_mean, state_std
    )
    env = wrap_env(env, state_mean=state_mean, state_std=state_std)
    replay_buffer = ReplayBuffer(
        state_dim,
        action_dim,
        config.buffer_size,
        config.device,
    )
    replay_buffer.load_d4rl_dataset(dataset)

    actor_critic_kwargs = {
        "state_dim": state_dim,
        "action_dim": action_dim,
        "hidden_dim": config.hidden_dim,
    }

    actor = Actor(**actor_critic_kwargs)
    actor.to(config.device)
    actor_optimizer = torch.optim.Adam(actor.parameters(), lr=config.learning_rate)
    critic_1 = Critic(**actor_critic_kwargs)
    critic_2 = Critic(**actor_critic_kwargs)
    critic_1.to(config.device)
    critic_2.to(config.device)
    critic_1_optimizer = torch.optim.Adam(critic_1.parameters(), lr=config.learning_rate)
    critic_2_optimizer = torch.optim.Adam(critic_2.parameters(), lr=config.learning_rate)

    awac = AdvantageWeightedActorCritic(
        actor=actor,
        actor_optimizer=actor_optimizer,
        critic_1=critic_1,
        critic_1_optimizer=critic_1_optimizer,
        critic_2=critic_2,
        critic_2_optimizer=critic_2_optimizer,
        gamma=config.gamma,
        tau=config.tau,
        awac_lambda=config.awac_lambda,
    )
    wandb_init(asdict(config))

    if config.checkpoints_path is not None:
        print(f"Checkpoints path: {config.checkpoints_path}")
        os.makedirs(config.checkpoints_path, exist_ok=True)
        with open(os.path.join(config.checkpoints_path, "config.yaml"), "w") as f:
            pyrallis.dump(config, f)

    for t in trange(config.num_train_ops, ncols=80):
        batch = replay_buffer.sample(config.batch_size)
        batch = [b.to(config.device) for b in batch]
        update_result = awac.update(batch)
        wandb.log(update_result, step=t)
        if (t + 1) % config.eval_frequency == 0:
            eval_scores = eval_actor(
                env, actor, config.device, config.n_test_episodes, config.test_seed
            )

            wandb.log({"eval_score": eval_scores.mean()}, step=t)
            if hasattr(env, "get_normalized_score"):
                normalized_eval_scores = env.get_normalized_score(eval_scores) * 100.0
                wandb.log(
                    {"d4rl_normalized_score": normalized_eval_scores.mean()}, step=t
                )

            if config.checkpoints_path is not None:
                torch.save(
                    awac.state_dict(),
                    os.path.join(config.checkpoints_path, f"checkpoint_{t}.pt"),
                )

    wandb.finish()


if __name__ == "__main__":
    train()