SAC-discrete implementation

.pre-commit-config.yaml

@@ -35,7 +35,7 @@ repos:
     hooks:
       - id: codespell
         args:
-          - --ignore-words-list=nd,reacher,thist,ths,magent
+          - --ignore-words-list=nd,reacher,thist,ths,magent,Ba
           - --skip=docs/css/termynal.css,docs/js/termynal.js,docs/get-started/CleanRL_Huggingface_Integration_Demo.ipynb
   - repo: https://github.com/python-poetry/poetry
     rev: 1.2.1
@@ -79,4 +79,4 @@ repos:
       - id: poetry-export
         name: poetry-export requirements-cloud.txt
         args: ["--without-hashes", "-o", "requirements/requirements-cloud.txt", "--with", "cloud"]
-        stages: [manual]
+        stages: [manual]

README.md

@@ -84,6 +84,7 @@ poetry install --with atari
 python cleanrl/dqn_atari.py --env-id BreakoutNoFrameskip-v4
 python cleanrl/c51_atari.py --env-id BreakoutNoFrameskip-v4
 python cleanrl/ppo_atari.py --env-id BreakoutNoFrameskip-v4
+python cleanrl/sac_atari.py --env-id BreakoutNoFrameskip-v4
 
 # NEW: 3-4x side-effects free speed up with envpool's atari (only available to linux)
 poetry install --with envpool
@@ -129,14 +130,15 @@ You may also use a prebuilt development environment hosted in Gitpod:
 | | [`ppo_pettingzoo_ma_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_pettingzoo_ma_atari.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_pettingzoo_ma_ataripy)
 | | [`ppo_continuous_action_isaacgym.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_continuous_action_isaacgym/ppo_continuous_action_isaacgym.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_continuous_action_isaacgympy)
 | ✅ [Deep Q-Learning (DQN)](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf) |  [`dqn.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/dqn/#dqnpy) |
-| |  [`dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_ataripy) |
+| | [`dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_ataripy) |
 | | [`dqn_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_jax.py), [docs](https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_jaxpy) |
 | | [`dqn_atari_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari_jax.py), [docs](https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_atari_jaxpy) |
 | ✅ [Categorical DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf) |  [`c51.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/c51/#c51py) |
 | |  [`c51_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/c51/#c51_ataripy) |
 | | [`c51_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_jax.py), [docs](https://docs.cleanrl.dev/rl-algorithms/c51/#c51_jaxpy) |
 | | [`c51_atari_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari_jax.py), [docs](https://docs.cleanrl.dev/rl-algorithms/c51/#c51_atari_jaxpy) |
 | ✅ [Soft Actor-Critic (SAC)](https://arxiv.org/pdf/1812.05905.pdf) |  [`sac_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sac_continuous_action.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/sac/#sac_continuous_actionpy) |
+| |  [`sac_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sac_atari.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/sac/#sac_atarinpy) |
 | ✅ [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/pdf/1509.02971.pdf) |  [`ddpg_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/ddpg/#ddpg_continuous_actionpy) |
 | | [`ddpg_continuous_action_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action_jax.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/ddpg/#ddpg_continuous_action_jaxpy)
 | ✅ [Twin Delayed Deep Deterministic Policy Gradient (TD3)](https://arxiv.org/pdf/1802.09477.pdf) |  [`td3_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action.py),  [docs](https://docs.cleanrl.dev/rl-algorithms/td3/#td3_continuous_actionpy) |

benchmark/sac_atari.sh

@@ -0,0 +1,6 @@
+poetry install --with atari
+OMP_NUM_THREADS=1 python -m cleanrl_utils.benchmark \
+    --env-ids PongNoFrameskip-v4 BreakoutNoFrameskip-v4 BeamRiderNoFrameskip-v4 \
+    --command "poetry run python cleanrl/sac_atari.py --cuda True --track" \
+    --num-seeds 3 \
+    --workers 2

cleanrl/sac_atari.py

@@ -0,0 +1,342 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/sac/#sac_ataripy
+import argparse
+import os
+import random
+import time
+from distutils.util import strtobool
+
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from stable_baselines3.common.atari_wrappers import (
+    ClipRewardEnv,
+    EpisodicLifeEnv,
+    FireResetEnv,
+    MaxAndSkipEnv,
+    NoopResetEnv,
+)
+from stable_baselines3.common.buffers import ReplayBuffer
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+
+def parse_args():
+    # fmt: off
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help="the name of this experiment")
+    parser.add_argument("--seed", type=int, default=1, 
+        help="seed of the experiment")
+    parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, `torch.backends.cudnn.deterministic=False`")
+    parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, cuda will be enabled by default")
+    parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="if toggled, this experiment will be tracked with Weights and Biases")
+    parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument("--wandb-entity", type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="weather to capture videos of the agent performances (check out `videos` folder)")
+
+    # Algorithm specific arguments
+    parser.add_argument("--env-id", type=str, default="BeamRiderNoFrameskip-v4",
+        help="the id of the environment")
+    parser.add_argument("--total-timesteps", type=int, default=5000000,
+        help="total timesteps of the experiments")
+    parser.add_argument("--buffer-size", type=int, default=int(1e6),
+        help="the replay memory buffer size") # smaller than in original paper but evaluation is done only for 100k steps anyway
+    parser.add_argument("--gamma", type=float, default=0.99,
+        help="the discount factor gamma")
+    parser.add_argument("--tau", type=float, default=1.0,
+        help="target smoothing coefficient (default: 1)") # Default is 1 to perform replacement update
+    parser.add_argument("--batch-size", type=int, default=64,
+        help="the batch size of sample from the reply memory")
+    parser.add_argument("--learning-starts", type=int, default=2e4,
+        help="timestep to start learning")
+    parser.add_argument("--policy-lr", type=float, default=3e-4,
+        help="the learning rate of the policy network optimizer")
+    parser.add_argument("--q-lr", type=float, default=3e-4,
+        help="the learning rate of the Q network network optimizer")
+    parser.add_argument("--update-frequency", type=int, default=4,
+        help="the frequency of training updates")
+    parser.add_argument("--target-network-frequency", type=int, default=8000,
+        help="the frequency of updates for the target networks")
+    parser.add_argument("--alpha", type=float, default=0.2,
+        help="Entropy regularization coefficient.")
+    parser.add_argument("--autotune", type=lambda x:bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="automatic tuning of the entropy coefficient")
+    parser.add_argument("--target-entropy-scale", type=float, default=0.89,
+        help="coefficient for scaling the autotune entropy target")
+    args = parser.parse_args()
+    # fmt: on
+    return args
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        env = NoopResetEnv(env, noop_max=30)
+        env = MaxAndSkipEnv(env, skip=4)
+        env = EpisodicLifeEnv(env)
+        if "FIRE" in env.unwrapped.get_action_meanings():
+            env = FireResetEnv(env)
+        env = ClipRewardEnv(env)
+        env = gym.wrappers.ResizeObservation(env, (84, 84))
+        env = gym.wrappers.GrayScaleObservation(env)
+        env = gym.wrappers.FrameStack(env, 4)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+
+    return thunk
+
+
+def layer_init(layer, bias_const=0.0):
+    nn.init.kaiming_normal_(layer.weight)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
+# ALGO LOGIC: initialize agent here:
+# NOTE: Sharing a CNN encoder between Actor and Critics is not recommended for SAC without stopping actor gradients
+# See the SAC+AE paper https://arxiv.org/abs/1910.01741 for more info
+# TL;DR The actor's gradients mess up the representation when using a joint encoder
+class SoftQNetwork(nn.Module):
+    def __init__(self, envs):
+        super().__init__()
+        obs_shape = envs.single_observation_space.shape
+        self.conv = nn.Sequential(
+            layer_init(nn.Conv2d(obs_shape[0], 32, kernel_size=8, stride=4)),
+            nn.ReLU(),
+            layer_init(nn.Conv2d(32, 64, kernel_size=4, stride=2)),
+            nn.ReLU(),
+            layer_init(nn.Conv2d(64, 64, kernel_size=3, stride=1)),
+            nn.Flatten(),
+        )
+
+        with torch.inference_mode():
+            output_dim = self.conv(torch.zeros(1, *obs_shape)).shape[1]
+
+        self.fc1 = layer_init(nn.Linear(output_dim, 512))
+        self.fc_q = layer_init(nn.Linear(512, envs.single_action_space.n))
+
+    def forward(self, x):
+        x = F.relu(self.conv(x / 255.0))
+        x = F.relu(self.fc1(x))
+        q_vals = self.fc_q(x)
+        return q_vals
+
+
+class Actor(nn.Module):
+    def __init__(self, envs):
+        super().__init__()
+        obs_shape = envs.single_observation_space.shape
+        self.conv = nn.Sequential(
+            layer_init(nn.Conv2d(obs_shape[0], 32, kernel_size=8, stride=4)),
+            nn.ReLU(),
+            layer_init(nn.Conv2d(32, 64, kernel_size=4, stride=2)),
+            nn.ReLU(),
+            layer_init(nn.Conv2d(64, 64, kernel_size=3, stride=1)),
+            nn.Flatten(),
+        )
+
+        with torch.inference_mode():
+            output_dim = self.conv(torch.zeros(1, *obs_shape)).shape[1]
+
+        self.fc1 = layer_init(nn.Linear(output_dim, 512))
+        self.fc_logits = layer_init(nn.Linear(512, envs.single_action_space.n))
+
+    def forward(self, x):
+        x = F.relu(self.conv(x))
+        x = F.relu(self.fc1(x))
+        logits = self.fc_logits(x)
+
+        return logits
+
+    def get_action(self, x):
+        logits = self(x / 255.0)
+        policy_dist = Categorical(logits=logits)
+        action = policy_dist.sample()
+        # Action probabilities for calculating the adapted soft-Q loss
+        action_probs = policy_dist.probs
+        log_prob = F.log_softmax(logits, dim=1)
+        return action, log_prob, action_probs
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    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.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    actor = Actor(envs).to(device)
+    qf1 = SoftQNetwork(envs).to(device)
+    qf2 = SoftQNetwork(envs).to(device)
+    qf1_target = SoftQNetwork(envs).to(device)
+    qf2_target = SoftQNetwork(envs).to(device)
+    qf1_target.load_state_dict(qf1.state_dict())
+    qf2_target.load_state_dict(qf2.state_dict())
+    # TRY NOT TO MODIFY: eps=1e-4 increases numerical stability
+    q_optimizer = optim.Adam(list(qf1.parameters()) + list(qf2.parameters()), lr=args.q_lr, eps=1e-4)
+    actor_optimizer = optim.Adam(list(actor.parameters()), lr=args.policy_lr, eps=1e-4)
+
+    # Automatic entropy tuning
+    if args.autotune:
+        target_entropy = -args.target_entropy_scale * torch.log(1 / torch.tensor(envs.single_action_space.n))
+        log_alpha = torch.zeros(1, requires_grad=True, device=device)
+        alpha = log_alpha.exp().item()
+        a_optimizer = optim.Adam([log_alpha], lr=args.q_lr, eps=1e-4)
+    else:
+        alpha = args.alpha
+
+    rb = ReplayBuffer(
+        args.buffer_size,
+        envs.single_observation_space,
+        envs.single_action_space,
+        device,
+        handle_timeout_termination=True,
+    )
+    start_time = time.time()
+
+    # TRY NOT TO MODIFY: start the game
+    obs = envs.reset()
+    for global_step in range(args.total_timesteps):
+        # ALGO LOGIC: put action logic here
+        if global_step < args.learning_starts:
+            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
+        else:
+            actions, _, _ = actor.get_action(torch.Tensor(obs).to(device))
+            actions = actions.detach().cpu().numpy()
+
+        # TRY NOT TO MODIFY: execute the game and log data.
+        next_obs, rewards, dones, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        for info in infos:
+            if "episode" in info.keys():
+                print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
+                writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
+                writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
+                break
+
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
+        real_next_obs = next_obs.copy()
+        for idx, d in enumerate(dones):
+            if d:
+                real_next_obs[idx] = infos[idx]["terminal_observation"]
+        rb.add(obs, real_next_obs, actions, rewards, dones, infos)
+
+        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
+        obs = next_obs
+
+        # ALGO LOGIC: training.
+        if global_step > args.learning_starts:
+            if global_step % args.update_frequency == 0:
+                data = rb.sample(args.batch_size)
+                # CRITIC training
+                with torch.no_grad():
+                    _, next_state_log_pi, next_state_action_probs = actor.get_action(data.next_observations)
+                    qf1_next_target = qf1_target(data.next_observations)
+                    qf2_next_target = qf2_target(data.next_observations)
+                    # we can use the action probabilities instead of MC sampling to estimate the expectation
+                    min_qf_next_target = next_state_action_probs * (
+                        torch.min(qf1_next_target, qf2_next_target) - alpha * next_state_log_pi
+                    )
+                    # adapt Q-target for discrete Q-function
+                    min_qf_next_target = min_qf_next_target.sum(dim=1)
+                    next_q_value = data.rewards.flatten() + (1 - data.dones.flatten()) * args.gamma * (min_qf_next_target)
+
+                # use Q-values only for the taken actions
+                qf1_values = qf1(data.observations)
+                qf2_values = qf2(data.observations)
+                qf1_a_values = qf1_values.gather(1, data.actions.long()).view(-1)
+                qf2_a_values = qf2_values.gather(1, data.actions.long()).view(-1)
+                qf1_loss = F.mse_loss(qf1_a_values, next_q_value)
+                qf2_loss = F.mse_loss(qf2_a_values, next_q_value)
+                qf_loss = qf1_loss + qf2_loss
+
+                q_optimizer.zero_grad()
+                qf_loss.backward()
+                q_optimizer.step()
+
+                # ACTOR training
+                _, log_pi, action_probs = actor.get_action(data.observations)
+                with torch.no_grad():
+                    qf1_values = qf1(data.observations)
+                    qf2_values = qf2(data.observations)
+                    min_qf_values = torch.min(qf1_values, qf2_values)
+                # no need for reparameterization, the expectation can be calculated for discrete actions
+                actor_loss = (action_probs * ((alpha * log_pi) - min_qf_values)).mean()
+
+                actor_optimizer.zero_grad()
+                actor_loss.backward()
+                actor_optimizer.step()
+
+                if args.autotune:
+                    # re-use action probabilities for temperature loss
+                    alpha_loss = (action_probs.detach() * (-log_alpha * (log_pi + target_entropy).detach())).mean()
+
+                    a_optimizer.zero_grad()
+                    alpha_loss.backward()
+                    a_optimizer.step()
+                    alpha = log_alpha.exp().item()
+
+            # update the target networks
+            if global_step % args.target_network_frequency == 0:
+                for param, target_param in zip(qf1.parameters(), qf1_target.parameters()):
+                    target_param.data.copy_(args.tau * param.data + (1 - args.tau) * target_param.data)
+                for param, target_param in zip(qf2.parameters(), qf2_target.parameters()):
+                    target_param.data.copy_(args.tau * param.data + (1 - args.tau) * target_param.data)
+
+            if global_step % 100 == 0:
+                writer.add_scalar("losses/qf1_values", qf1_a_values.mean().item(), global_step)
+                writer.add_scalar("losses/qf2_values", qf2_a_values.mean().item(), global_step)
+                writer.add_scalar("losses/qf1_loss", qf1_loss.item(), global_step)
+                writer.add_scalar("losses/qf2_loss", qf2_loss.item(), global_step)
+                writer.add_scalar("losses/qf_loss", qf_loss.item() / 2.0, global_step)
+                writer.add_scalar("losses/actor_loss", actor_loss.item(), global_step)
+                writer.add_scalar("losses/alpha", alpha, 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)
+                if args.autotune:
+                    writer.add_scalar("losses/alpha_loss", alpha_loss.item(), global_step)
+
+    envs.close()
+    writer.close()