game2048_v2_ppo_action_mask.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_ataripy
import os
import random
import time
from dataclasses import dataclass

import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import tyro
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter

import gym_game2048
from gym_game2048.wrappers import Normalize2048, RewardConverter, TerminateIllegal
from gymnasium.wrappers import TransformReward, DtypeObservation, ReshapeObservation
from tqdm import tqdm


@dataclass
class Args:
    exp_name: str = os.path.basename(__file__)[: -len(".py")]
    """the name of this experiment"""
    seed: int = 1
    """seed of the experiment"""
    torch_deterministic: bool = False
    """if toggled, `torch.backends.cudnn.deterministic=False`"""
    cuda: bool = True
    """if toggled, cuda will be enabled by default"""
    track: bool = True
    """if toggled, this experiment will be tracked with Weights and Biases"""
    wandb_project_name: str = "game2048_v2"
    """the wandb's project name"""
    wandb_entity: str = None
    """the entity (team) of wandb's project"""
    capture_video: bool = False
    """whether to capture videos of the agent performances (check out `videos` folder)"""

    # Algorithm specific arguments
    env_id: str = "gym_game2048/Game2048-v0"
    """the id of the environment"""
    total_timesteps: int = 400000000
    """total timesteps of the experiments"""
    learning_rate: float = 2.5e-4
    """the learning rate of the optimizer"""
    num_envs: int = 64
    """the number of parallel game environments"""
    num_steps: int = 128
    """the number of steps to run in each environment per policy rollout"""
    anneal_lr: bool = False
    """Toggle learning rate annealing for policy and value networks"""
    gamma: float = 0.99
    """the discount factor gamma"""
    gae_lambda: float = 0.95
    """the lambda for the general advantage estimation"""
    num_minibatches: int = 4
    """the number of mini-batches"""
    update_epochs: int = 4
    """the K epochs to update the policy"""
    norm_adv: bool = True
    """Toggles advantages normalization"""
    clip_coef: float = 0.2
    """the surrogate clipping coefficient"""
    clip_vloss: bool = True
    """Toggles whether or not to use a clipped loss for the value function, as per the paper."""
    ent_coef: float = 0.01
    """coefficient of the entropy"""
    vf_coef: float = 0.5
    """coefficient of the value function"""
    max_grad_norm: float = 0.5
    """the maximum norm for the gradient clipping"""
    target_kl: float | None = None
    """the target KL divergence threshold"""

    # to be filled in runtime
    batch_size: int = 0
    """the batch size (computed in runtime)"""
    minibatch_size: int = 0
    """the mini-batch size (computed in runtime)"""
    num_iterations: int = 0
    """the number of iterations (computed in runtime)"""

    goal: int = 2**15
    """Goal of gmae"""

    # track interval
    log_charts_interval: int = 1000
    """Record interval for chart"""
    log_losses_interval: int = 20
    """Record interval for losses"""
    record_interval: int = 100
    """Record interval for RecordVideo"""

    # network setting
    linear_size: int = 512
    """size of FCN"""
    cnn_channel: int = 128
    """size of CNN channel"""

    load_model: str = ""
    """whether to load model `runs/{run_name}` folder"""

    network: str = "cnn"
    """kind of network"""
    term_rew: float = -5.0
    """negative reward on termination"""

    action_mask: bool = True
    """whether to mask action"""


def make_env(env_id, idx, capture_video, run_name):
    def thunk():
        if capture_video and idx == 0:
            env = gym.make(env_id, goal=args.goal, render_mode="rgb_array")
            env = gym.wrappers.RecordVideo(
                env,
                f"videos/{run_name}",
                episode_trigger=lambda x: x % args.record_interval == 0,
                # disable_logger=False,
            )
        else:
            env = gym.make(env_id, goal=args.goal)
        # reward
        env = RewardConverter(env, div_pos_rew=512, term_rew=args.term_rew)
        # observation
        env = ReshapeObservation(env, (1, 4, 4))
        env = DtypeObservation(env, np.float32)
        env = Normalize2048(env)
        # util
        env = gym.wrappers.RecordEpisodeStatistics(env)
        return env

    return thunk


def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
    torch.nn.init.orthogonal_(layer.weight, std)
    torch.nn.init.constant_(layer.bias, bias_const)
    return layer


class CategoricalMasked(Categorical):
    def __init__(self, probs=None, logits=None, validate_args=None, masks=[]):
        self.masks = masks
        if len(self.masks) == 0:
            super(CategoricalMasked, self).__init__(probs, logits, validate_args)
        else:
            self.masks = masks.type(torch.BoolTensor).to(device)
            logits = torch.where(self.masks, logits, torch.tensor(-1e8).to(device))
            super(CategoricalMasked, self).__init__(probs, logits, validate_args)

    def entropy(self):
        if len(self.masks) == 0:
            return super(CategoricalMasked, self).entropy()
        p_log_p = self.logits * self.probs
        p_log_p = torch.where(self.masks, p_log_p, torch.tensor(0.0).to(device))
        return -p_log_p.sum(-1)


class Agent(nn.Module):
    def __init__(self, envs):
        super().__init__()
        if args.network == "cnn":
            self.network = nn.Sequential(
                layer_init(nn.Conv2d(1, args.cnn_channel, 3)),
                nn.ReLU(),
                nn.Flatten(),
                layer_init(nn.Linear(args.cnn_channel * 2 * 2, args.linear_size)),
                nn.ReLU(),
            )
        elif args.network == "linear":
            self.network = nn.Sequential(
                nn.Flatten(),
                layer_init(nn.Linear(16, args.linear_size)),
                nn.ReLU(),
                layer_init(nn.Linear(args.linear_size, args.linear_size)),
                nn.ReLU(),
            )
        self.actor = layer_init(nn.Linear(args.linear_size, envs.single_action_space.n), std=0.01)
        self.critic = layer_init(nn.Linear(args.linear_size, 1), std=1)

    def get_value(self, x):
        return self.critic(self.network(x))

    def get_action_and_value(self, x, action=None, invalid_action_mask=None):
        hidden = self.network(x)
        logits = self.actor(hidden)
        # probs = Categorical(logits=logits)
        probs = CategoricalMasked(logits=logits, masks=invalid_action_mask)
        if action is None:
            action = probs.sample()
        return action, probs.log_prob(action), probs.entropy(), self.critic(hidden)


if __name__ == "__main__":
    charts_count = 0
    losses_count = 0
    args = tyro.cli(Args)
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.num_minibatches)
    args.num_iterations = args.total_timesteps // args.batch_size
    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.track:
        import wandb

        wandb.init(
            project=args.wandb_project_name,
            entity=args.wandb_entity,
            sync_tensorboard=True,
            config=vars(args),
            name=run_name,
            # monitor_gym=True,
            save_code=True,
        )
    writer = SummaryWriter(f"runs/{run_name}")
    writer.add_text(
        "hyperparameters",
        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
    )

    # TRY NOT TO MODIFY: seeding
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.backends.cudnn.deterministic = args.torch_deterministic

    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")

    # env setup
    envs = gym.vector.AsyncVectorEnv(
        [make_env(args.env_id, i, args.capture_video, run_name) for i in range(args.num_envs)],
    )
    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

    agent = Agent(envs).to(device)
    if args.load_model:
        agent.load_state_dict(torch.load(args.load_model))
    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)

    # ALGO Logic: Storage setup
    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
    invalid_action_masks = torch.zeros((args.num_steps, args.num_envs) + (envs.single_action_space.n,)).to(device)

    # TRY NOT TO MODIFY: start the game
    global_step = 0
    start_time = time.time()
    next_obs, infos = envs.reset(seed=args.seed)
    next_obs = torch.Tensor(next_obs).to(device)
    next_done = torch.zeros(args.num_envs).to(device)

    for iteration in tqdm(range(1, args.num_iterations + 1)):
        # Annealing the rate if instructed to do so.
        if args.anneal_lr:
            frac = 1.0 - (iteration - 1.0) / args.num_iterations
            lrnow = frac * args.learning_rate
            optimizer.param_groups[0]["lr"] = lrnow

        for step in range(0, args.num_steps):
            global_step += args.num_envs
            obs[step] = next_obs
            dones[step] = next_done
            invalid_action_masks[step] = torch.Tensor(infos["action_mask"]).to(device)

            # ALGO LOGIC: action logic
            with torch.no_grad():
                action, logprob, _, value = agent.get_action_and_value(
                    next_obs, invalid_action_mask=invalid_action_masks[step]
                )
                values[step] = value.flatten()
            actions[step] = action
            logprobs[step] = logprob

            # TRY NOT TO MODIFY: execute the game and log data.
            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
            next_done = np.logical_or(terminations, truncations)
            rewards[step] = torch.tensor(reward).to(device).view(-1)
            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)

            if "episode" in infos:
                for i, b in enumerate(infos["episode"]["_r"]):
                    if b:
                        if charts_count % args.log_charts_interval == 0:
                            # print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
                            writer.add_scalar("charts/episodic_return", infos["episode"]["r"][i], global_step)
                            writer.add_scalar("charts/episodic_length", infos["episode"]["l"][i], global_step)
                            writer.add_scalar("charts/score", infos["score"][i], global_step)
                            writer.add_scalar("charts/max_number", infos["max"][i], global_step)
                            charts_count = 1
                        else:
                            charts_count += 1

        # bootstrap value if not done
        with torch.no_grad():
            next_value = agent.get_value(next_obs).reshape(1, -1)
            advantages = torch.zeros_like(rewards).to(device)
            lastgaelam = 0
            for t in reversed(range(args.num_steps)):
                if t == args.num_steps - 1:
                    nextnonterminal = 1.0 - next_done
                    nextvalues = next_value
                else:
                    nextnonterminal = 1.0 - dones[t + 1]
                    nextvalues = values[t + 1]
                delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
                advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
            returns = advantages + values

        # flatten the batch
        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
        b_logprobs = logprobs.reshape(-1)
        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
        b_advantages = advantages.reshape(-1)
        b_returns = returns.reshape(-1)
        b_values = values.reshape(-1)
        b_invalid_action_masks = invalid_action_masks.reshape((-1, envs.single_action_space.n))

        # Optimizing the policy and value network
        b_inds = np.arange(args.batch_size)
        clipfracs = []
        for epoch in range(args.update_epochs):
            np.random.shuffle(b_inds)
            for start in range(0, args.batch_size, args.minibatch_size):
                end = start + args.minibatch_size
                mb_inds = b_inds[start:end]

                _, newlogprob, entropy, newvalue = agent.get_action_and_value(
                    b_obs[mb_inds], b_actions.long()[mb_inds], b_invalid_action_masks[mb_inds]
                )
                logratio = newlogprob - b_logprobs[mb_inds]
                ratio = logratio.exp()

                with torch.no_grad():
                    # calculate approx_kl http://joschu.net/blog/kl-approx.html
                    old_approx_kl = (-logratio).mean()
                    approx_kl = ((ratio - 1) - logratio).mean()
                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]

                mb_advantages = b_advantages[mb_inds]
                if args.norm_adv:
                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)

                # Policy loss
                pg_loss1 = -mb_advantages * ratio
                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
                pg_loss = torch.max(pg_loss1, pg_loss2).mean()

                # Value loss
                newvalue = newvalue.view(-1)
                if args.clip_vloss:
                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
                    v_clipped = b_values[mb_inds] + torch.clamp(
                        newvalue - b_values[mb_inds],
                        -args.clip_coef,
                        args.clip_coef,
                    )
                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
                    v_loss = 0.5 * v_loss_max.mean()
                else:
                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()

                entropy_loss = entropy.mean()
                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef

                optimizer.zero_grad()
                loss.backward()
                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
                optimizer.step()

            if args.target_kl is not None and approx_kl > args.target_kl:
                break

        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
        var_y = np.var(y_true)
        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y

        # TRY NOT TO MODIFY: record rewards for plotting purposes
        if losses_count % args.log_losses_interval == 0:
            writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
            writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
            writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
            writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
            writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
            writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
            writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
            writer.add_scalar("losses/explained_variance", explained_var, global_step)
            # print("SPS:", int(global_step / (time.time() - start_time)))
            writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
            losses_count = 1
        else:
            losses_count += 1

        if iteration % (args.num_iterations // 5) == 0:
            model_path = f"runs/{run_name}/cleanrl_{args.exp_name}_{iteration}.pt"
            torch.save(agent.state_dict(), model_path)
            print(f"model saved to {model_path}")
    envs.close()
    writer.close()