a series of modifications to get the continual world up to gymnasium, recent metaworld, and the sac model running

basic_tf.py

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+# TensorFlow and tf.keras sample to make sure the tf install is running
+import tensorflow as tf
+
+# Helper libraries
+import numpy as np
+import matplotlib.pyplot as plt
+
+fashion_mnist = tf.keras.datasets.fashion_mnist
+
+(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
+
+class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
+               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+
+train_images = train_images / 255.0
+
+test_images = test_images / 255.0
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Flatten(input_shape=(28, 28)),
+    tf.keras.layers.Dense(128, activation='relu'),
+    tf.keras.layers.Dense(10)
+])
+
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+
+model.fit(train_images, train_labels, epochs=10)
+
+test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+
+print('\nTest accuracy:', test_acc)
+
+

clean_sac.py

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+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/sac/#sac_continuous_actionpy
+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.nn.functional as F
+import torch.optim as optim
+import tyro
+from stable_baselines3.common.buffers import ReplayBuffer
+from torch.utils.tensorboard import SummaryWriter
+
+from continualworld.envs import get_cl_env, get_single_env
+from continualworld.tasks import TASK_SEQS
+
+
+@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 = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """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 = "Hopper-v4"
+    """the environment id of the task"""
+    # num_envs: int = 2
+    # """the environment id of the task"""
+    total_timesteps: int = 1000000
+    """total timesteps of the experiments"""
+    buffer_size: int = int(1e6)
+    """the replay memory buffer size"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    tau: float = 0.005
+    """target smoothing coefficient (default: 0.005)"""
+    batch_size: int = 256
+    """the batch size of sample from the reply memory"""
+    learning_starts: int = 5e3
+    """timestep to start learning"""
+    policy_lr: float = 3e-4
+    """the learning rate of the policy network optimizer"""
+    q_lr: float = 1e-3
+    """the learning rate of the Q network network optimizer"""
+    policy_frequency: int = 2
+    """the frequency of training policy (delayed)"""
+    target_network_frequency: int = 1  # Denis Yarats' implementation delays this by 2.
+    """the frequency of updates for the target nerworks"""
+    noise_clip: float = 0.5
+    """noise clip parameter of the Target Policy Smoothing Regularization"""
+    alpha: float = 0.2
+    """Entropy regularization coefficient."""
+    autotune: bool = True
+    """automatic tuning of the entropy coefficient"""
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        if capture_video and idx == 0:
+            env = gym.make(env_id, render_mode="rgb_array")
+            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        else:
+            env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        env.action_space.seed(seed)
+        return env
+
+    return thunk
+
+
+# ALGO LOGIC: initialize agent here:
+class SoftQNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        self.fc1 = nn.Linear(np.array(env.single_observation_space.shape).prod() + np.prod(env.single_action_space.shape), 256)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 1)
+
+    def forward(self, x, a):
+        x = torch.cat([x, a], 1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+LOG_STD_MAX = 2
+LOG_STD_MIN = -5
+
+
+class Actor(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        ## TODO balloch: if making continual world vectorized, change "action_space" and "observation_space" back to "single_action_space" and"single_observation_space"
+        self.fc1 = nn.Linear(np.array(env.single_observation_space.shape).prod(), 256)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc_mean = nn.Linear(256, np.prod(env.single_action_space.shape))
+        self.fc_logstd = nn.Linear(256, np.prod(env.single_action_space.shape))
+        # action rescaling
+        self.register_buffer(
+            "action_scale", torch.tensor((env.action_space.high - env.action_space.low) / 2.0, dtype=torch.float32)
+        )
+        self.register_buffer(
+            "action_bias", torch.tensor((env.action_space.high + env.action_space.low) / 2.0, dtype=torch.float32)
+        )
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        mean = self.fc_mean(x)
+        log_std = self.fc_logstd(x)
+        log_std = torch.tanh(log_std)
+        log_std = LOG_STD_MIN + 0.5 * (LOG_STD_MAX - LOG_STD_MIN) * (log_std + 1)  # From SpinUp / Denis Yarats
+
+        return mean, log_std
+
+    def get_action(self, x):
+        mean, log_std = self(x)
+        std = log_std.exp()
+        normal = torch.distributions.Normal(mean, std)
+        x_t = normal.rsample()  # for reparameterization trick (mean + std * N(0,1))
+        y_t = torch.tanh(x_t)
+        action = y_t * self.action_scale + self.action_bias
+        log_prob = normal.log_prob(x_t)
+        # Enforcing Action Bound
+        log_prob -= torch.log(self.action_scale * (1 - y_t.pow(2)) + 1e-6)
+        if len(log_prob.shape) < 2:  # for flat vectors
+            log_prob = log_prob.sum()
+        else:
+            log_prob = log_prob.sum(1, keepdim=True)
+        mean = torch.tanh(mean) * self.action_scale + self.action_bias
+        return action, log_prob, mean
+
+
+if __name__ == "__main__":
+    import stable_baselines3 as sb3
+
+    if sb3.__version__ < "2.0":
+        raise ValueError(
+            """Ongoing migration: run the following command to install the new dependencies:
+poetry run pip install "stable_baselines3==2.0.0a1"
+"""
+        )
+
+    args = tyro.cli(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
+    if args.env_id == 'CW10':
+        tasks='CW10'  # TODO unhardcode this
+        if tasks is not None:
+            tasks = TASK_SEQS[tasks]
+        steps_per_task = 1000
+        envs = get_cl_env(tasks, steps_per_task)
+        # Consider normalizing test envs in the future.
+        num_tasks = len(tasks)
+        test_envs = [
+            get_single_env(task, one_hot_idx=i, one_hot_len=num_tasks) for i, task in enumerate(tasks)
+        ]
+        args.total_timesteps = steps_per_task * len(tasks)
+        envs.single_observation_space = envs.observation_space
+        envs.single_action_space = envs.action_space
+        num_envs = 1  # One at a time
+
+    else:
+        num_envs = 1  # should be an arg for vectorization...
+        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.Box), "only continuous action space is supported"
+
+    # Ensure matching types, has "kind" syntax per https://numpy.org/doc/stable/reference/generated/numpy.dtype.kind.html
+    envs.single_observation_space.dtype = np.dtype('f4')
+    if args.env_id == 'CW10':
+        envs.single_action_space.dtype = np.dtype('f4')
+
+
+
+    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())
+    q_optimizer = optim.Adam(list(qf1.parameters()) + list(qf2.parameters()), lr=args.q_lr)
+    actor_optimizer = optim.Adam(list(actor.parameters()), lr=args.policy_lr)
+
+    # Automatic entropy tuning
+    if args.autotune:
+        target_entropy = -torch.prod(torch.Tensor(envs.single_action_space.shape).to(device)).item()
+        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)
+    else:
+        alpha = args.alpha
+
+    rb = ReplayBuffer(
+        buffer_size=args.buffer_size,
+        observation_space=envs.single_observation_space,
+        action_space=envs.single_action_space,
+        device=device,
+        n_envs=num_envs, #envs.num_envs
+        handle_timeout_termination=False,
+    )
+
+
+    start_time = time.time()
+
+    # TRY NOT TO MODIFY: start the game
+    obs, _ = envs.reset(seed=args.seed)
+    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(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.
+        if args.env_id == 'CW10':
+            next_obs, rewards, terminations, truncations, infos = envs.step(actions.flatten())
+        else:
+            next_obs, rewards, terminations, truncations, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        if args.env_id == 'CW10':  # Non-vector envs
+            if 'episode' in infos:
+                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
+
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                if info:
+                    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 `final_observation`
+        real_next_obs = next_obs.copy()
+        if args.env_id != 'CW10':  ## only for vectorized environments
+            for idx, trunc in enumerate(truncations):
+                if trunc:
+                    real_next_obs[idx] = infos["final_observation"][idx]
+        else:
+            if truncations:
+                envs.reset()
+        rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
+
+        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
+        obs = next_obs
+
+        # ALGO LOGIC: training.
+        if global_step > args.learning_starts:
+            data = rb.sample(args.batch_size)
+            with torch.no_grad():
+                next_state_actions, next_state_log_pi, _ = actor.get_action(data.next_observations)
+                qf1_next_target = qf1_target(data.next_observations, next_state_actions)
+                qf2_next_target = qf2_target(data.next_observations, next_state_actions)
+                min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - alpha * next_state_log_pi
+                next_q_value = data.rewards.flatten() + (1 - data.dones.flatten()) * args.gamma * (min_qf_next_target).view(-1)
+
+            qf1_a_values = qf1(data.observations, data.actions).view(-1)
+            qf2_a_values = qf2(data.observations, data.actions).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
+
+            # optimize the model
+            q_optimizer.zero_grad()
+            qf_loss.backward()
+            q_optimizer.step()
+
+            if global_step % args.policy_frequency == 0:  # TD 3 Delayed update support
+                for _ in range(
+                    args.policy_frequency
+                ):  # compensate for the delay by doing 'actor_update_interval' instead of 1
+                    pi, log_pi, _ = actor.get_action(data.observations)
+                    qf1_pi = qf1(data.observations, pi)
+                    qf2_pi = qf2(data.observations, pi)
+                    min_qf_pi = torch.min(qf1_pi, qf2_pi)
+                    actor_loss = ((alpha * log_pi) - min_qf_pi).mean()
+
+                    actor_optimizer.zero_grad()
+                    actor_loss.backward()
+                    actor_optimizer.step()
+
+                    if args.autotune:
+                        with torch.no_grad():
+                            _, log_pi, _ = actor.get_action(data.observations)
+                        alpha_loss = (-log_alpha.exp() * (log_pi + target_entropy)).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()