pushing model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+rainbow_atari.cleanrl_model filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,79 @@
+---
+tags:
+- BreakoutNoFrameskip-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+library_name: cleanrl
+model-index:
+- name: RAINBOW
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BreakoutNoFrameskip-v4
+      type: BreakoutNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: 4.50 +/- 4.50
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **RAINBOW** Agent Playing **BreakoutNoFrameskip-v4**
+
+This is a trained model of a RAINBOW agent playing BreakoutNoFrameskip-v4.
+The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
+found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/rainbow_atari.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[rainbow_atari]"
+python -m cleanrl_utils.enjoy --exp-name rainbow_atari --env-id BreakoutNoFrameskip-v4
+```
+
+Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
+
+
+## Command to reproduce the training
+
+```bash
+curl -OL https://huggingface.co/qgallouedec/BreakoutNoFrameskip-v4-rainbow_atari-seed1/raw/main/rainbow_atari.py
+curl -OL https://huggingface.co/qgallouedec/BreakoutNoFrameskip-v4-rainbow_atari-seed1/raw/main/pyproject.toml
+curl -OL https://huggingface.co/qgallouedec/BreakoutNoFrameskip-v4-rainbow_atari-seed1/raw/main/poetry.lock
+poetry install --all-extras
+python rainbow_atari.py --learning-starts 100 --total-timesteps 5000 --save-model --upload-model --hf-entity qgallouedec
+```
+
+# Hyperparameters
+```python
+{'batch_size': 32,
+ 'buffer_size': 1000000,
+ 'capture_video': False,
+ 'cuda': True,
+ 'env_id': 'BreakoutNoFrameskip-v4',
+ 'exp_name': 'rainbow_atari',
+ 'gamma': 0.99,
+ 'hf_entity': 'qgallouedec',
+ 'learning_rate': 0.00025,
+ 'learning_starts': 100,
+ 'n_atoms': 51,
+ 'num_envs': 1,
+ 'save_model': True,
+ 'seed': 1,
+ 'target_network_frequency': 10000,
+ 'torch_deterministic': True,
+ 'total_timesteps': 5000,
+ 'track': False,
+ 'train_frequency': 4,
+ 'upload_model': True,
+ 'v_max': 10,
+ 'v_min': -10,
+ 'wandb_entity': None,
+ 'wandb_project_name': 'cleanRL'}
+```
+    

events.out.tfevents.1700431636.MacBook-Pro-de-Quentin.local.52615.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfa07a2b4c1146a00b269973643f3d22e86edac5852a0cc786fae5ed7930d365
+size 11272

pyproject.toml

@@ -0,0 +1,108 @@
+[tool.poetry]
+name = "cleanrl"
+version = "1.1.0"
+description = "High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features"
+authors = ["Costa Huang <costa.huang@outlook.com>"]
+packages = [
+    { include = "cleanrl" },
+    { include = "cleanrl_utils" },
+]
+keywords = ["reinforcement", "machine", "learning", "research"]
+license="MIT"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.7.1,<3.11"
+tensorboard = "^2.10.0"
+wandb = "^0.13.11"
+gym = "0.23.1"
+torch = ">=1.12.1"
+stable-baselines3 = "1.2.0"
+gymnasium = ">=0.28.1"
+moviepy = "^1.0.3"
+pygame = "2.1.0"
+huggingface-hub = "^0.11.1"
+rich = "<12.0"
+tenacity = "^8.2.2"
+
+ale-py = {version = "0.7.4", optional = true}
+AutoROM = {extras = ["accept-rom-license"], version = "^0.4.2", optional = true}
+opencv-python = {version = "^4.6.0.66", optional = true}
+procgen = {version = "^0.10.7", optional = true}
+pytest = {version = "^7.1.3", optional = true}
+mujoco = {version = "<=2.3.3", optional = true}
+imageio = {version = "^2.14.1", optional = true}
+free-mujoco-py = {version = "^2.1.6", optional = true}
+mkdocs-material = {version = "^8.4.3", optional = true}
+markdown-include = {version = "^0.7.0", optional = true}
+openrlbenchmark = {version = "^0.1.1b4", optional = true}
+jax = {version = "^0.3.17", optional = true}
+jaxlib = {version = "^0.3.15", optional = true}
+flax = {version = "^0.6.0", optional = true}
+optuna = {version = "^3.0.1", optional = true}
+optuna-dashboard = {version = "^0.7.2", optional = true}
+envpool = {version = "^0.6.4", optional = true}
+PettingZoo = {version = "1.18.1", optional = true}
+SuperSuit = {version = "3.4.0", optional = true}
+multi-agent-ale-py = {version = "0.1.11", optional = true}
+boto3 = {version = "^1.24.70", optional = true}
+awscli = {version = "^1.25.71", optional = true}
+shimmy = {version = ">=1.0.0", extras = ["dm-control"], optional = true}
+
+[tool.poetry.group.dev.dependencies]
+pre-commit = "^2.20.0"
+
+
+[tool.poetry.group.isaacgym]
+optional = true
+[tool.poetry.group.isaacgym.dependencies]
+isaacgymenvs = {git = "https://github.com/vwxyzjn/IsaacGymEnvs.git", rev = "poetry", python = ">=3.7.1,<3.10"}
+isaacgym = {path = "cleanrl/ppo_continuous_action_isaacgym/isaacgym", develop = true}
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.extras]
+atari = ["ale-py", "AutoROM", "opencv-python"]
+procgen = ["procgen"]
+plot = ["pandas", "seaborn"]
+pytest = ["pytest"]
+mujoco = ["mujoco", "imageio"]
+mujoco_py = ["free-mujoco-py"]
+jax = ["jax", "jaxlib", "flax"]
+docs = ["mkdocs-material", "markdown-include", "openrlbenchmark"]
+envpool = ["envpool"]
+optuna = ["optuna", "optuna-dashboard"]
+pettingzoo = ["PettingZoo", "SuperSuit", "multi-agent-ale-py"]
+cloud = ["boto3", "awscli"]
+dm_control = ["shimmy", "mujoco"]
+
+# dependencies for algorithm variant (useful when you want to run a specific algorithm)
+dqn = []
+dqn_atari = ["ale-py", "AutoROM", "opencv-python"]
+dqn_jax = ["jax", "jaxlib", "flax"]
+dqn_atari_jax = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax" # jax
+]
+c51 = []
+c51_atari = ["ale-py", "AutoROM", "opencv-python"]
+c51_jax = ["jax", "jaxlib", "flax"]
+c51_atari_jax = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax" # jax
+]
+ppo_atari_envpool_xla_jax_scan = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax", # jax
+    "envpool", # envpool
+]
+qdagger_dqn_atari_impalacnn = [
+    "ale-py", "AutoROM", "opencv-python"
+]
+qdagger_dqn_atari_jax_impalacnn = [
+    "ale-py", "AutoROM", "opencv-python", # atari
+    "jax", "jaxlib", "flax", # jax
+]

rainbow_atari.cleanrl_model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:463a757c64a65defb5532bc934cc088ef6d03eb2c7b4a683779abb9a7481f0b3
+size 40440331

rainbow_atari.py

@@ -0,0 +1,475 @@
+# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_ataripy
+import argparse
+import math
+import os
+import random
+import time
+from collections import deque
+from distutils.util import strtobool
+from types import SimpleNamespace
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+import torch.optim as optim
+from stable_baselines3.common.atari_wrappers import ClipRewardEnv, EpisodicLifeEnv, FireResetEnv, MaxAndSkipEnv, NoopResetEnv
+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="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to save model into the `runs/{run_name}` folder")
+    parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to upload the saved model to huggingface")
+    parser.add_argument("--hf-entity", type=str, default="",
+        help="the user or org name of the model repository from the Hugging Face Hub")
+
+    # Algorithm specific arguments
+    parser.add_argument("--env-id", type=str, default="BreakoutNoFrameskip-v4",
+        help="the id of the environment")
+    parser.add_argument("--total-timesteps", type=int, default=10000000,
+        help="total timesteps of the experiments")
+    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
+        help="the learning rate of the optimizer")
+    parser.add_argument("--num-envs", type=int, default=1,
+        help="the number of parallel game environments")
+    parser.add_argument("--n-atoms", type=int, default=51,
+        help="the number of atoms")
+    parser.add_argument("--v-min", type=float, default=-10,
+        help="the return lower bound")
+    parser.add_argument("--v-max", type=float, default=10,
+        help="the return upper bound")
+    parser.add_argument("--buffer-size", type=int, default=1000000,
+        help="the replay memory buffer size")
+    parser.add_argument("--gamma", type=float, default=0.99,
+        help="the discount factor gamma")
+    parser.add_argument("--target-network-frequency", type=int, default=10000,
+        help="the timesteps it takes to update the target network")
+    parser.add_argument("--batch-size", type=int, default=32,
+        help="the batch size of sample from the reply memory")
+    parser.add_argument("--learning-starts", type=int, default=80000,
+        help="timestep to start learning")
+    parser.add_argument("--train-frequency", type=int, default=4,
+        help="the frequency of training")
+    args = parser.parse_args()
+    # fmt: on
+    assert args.num_envs == 1, "vectorized envs are not supported at the moment"
+
+    return args
+
+
+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 = 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.action_space.seed(seed)
+        return env
+
+    return thunk
+
+
+class SumTree:
+    def __init__(self, capacity):
+        self.capacity = capacity  # Capacity of the sum tree (number of leaves)
+        self.tree = [0] * (2 * capacity)  # Binary tree representation
+        self.max_priority = 1.0  # Initial max priority for new experiences
+
+    def update(self, index, priority=None):
+        if priority is None:
+            priority = self.max_priority
+        tree_idx = index + self.capacity
+        change = priority - self.tree[tree_idx]
+        self.tree[tree_idx] = priority
+        self._propagate(tree_idx, change)
+        self.max_priority = max(self.max_priority, priority)
+
+    def _propagate(self, idx, change):
+        parent = idx // 2
+        while parent != 0:
+            self.tree[parent] += change
+            parent = parent // 2
+
+    def total(self):
+        return self.tree[1]  # The root of the tree holds the total sum
+
+    def get(self, s):
+        idx = 1
+        while idx < self.capacity:  # Keep moving down the tree to find the index
+            left = 2 * idx
+            right = left + 1
+            if self.tree[left] >= s:
+                idx = left
+            else:
+                s -= self.tree[left]
+                idx = right
+        return idx - self.capacity
+
+
+class PrioritizedReplayBuffer:
+    def __init__(self, size, device, alpha=0.5, beta_0=0.4, n_step=3, gamma=0.99):
+        self.size = size
+        self.device = device
+        self.alpha = alpha
+        self.beta_0 = beta_0
+        self.update_beta(0.0)
+        self.n_step = n_step
+        self.gamma = gamma
+
+        self.next_index = 0
+        self.sum_tree = SumTree(size)
+        self.observations = np.zeros((self.size, 4, 84, 84), dtype=np.uint8)
+        self.next_observations = np.zeros((self.size, 4, 84, 84), dtype=np.uint8)
+        self.actions = np.zeros((self.size, 1), dtype=np.int64)
+        self.rewards = np.zeros((self.size, 1), dtype=np.float32)
+        self.dones = np.zeros((self.size, 1), dtype=bool)
+
+        self.n_step_buffer = deque(maxlen=n_step)
+
+    def add(self, obs, next_obs, actions, rewards, dones, infos):
+        self.n_step_buffer.append((obs[0], next_obs[0], actions[0], rewards[0], dones[0], infos))
+
+        if len(self.n_step_buffer) < self.n_step and not dones[0]:
+            return
+
+        # Compute n-step return and the first state and action
+        rewards = [self.n_step_buffer[i][3] for i in range(len(self.n_step_buffer))]
+        n_step_return = sum([r * (self.gamma**i) for i, r in enumerate(rewards)])
+        obs, _, action, _, _, _ = self.n_step_buffer[0]
+        _, next_obs, _, _, done, _ = self.n_step_buffer[-1]
+
+        # Store the n-step transition
+        self.observations[self.next_index] = obs
+        self.next_observations[self.next_index] = next_obs
+        self.actions[self.next_index] = action
+        self.rewards[self.next_index] = n_step_return
+        self.dones[self.next_index] = done
+
+        # Get the max priority in the tree and set the new transition with max priority
+        self.sum_tree.update(self.next_index)
+        self.next_index = (self.next_index + 1) % self.size
+
+        if dones[0]:
+            self.n_step_buffer.clear()
+
+    def sample(self, batch_size):
+        segment = self.sum_tree.total() / batch_size
+        idxs = []
+        priorities = []
+        for i in range(batch_size):
+            a = segment * i
+            b = segment * (i + 1)
+            s = random.uniform(a, b)
+            idx = self.sum_tree.get(s)
+            idxs.append(idx)
+            leaf_idx = idx + self.size  # Adjusting index to point to the leaf node
+            priorities.append(self.sum_tree.tree[leaf_idx])
+
+        priorities = torch.tensor(priorities, dtype=torch.float32, device=self.device).unsqueeze(1)
+        sampling_probabilities = priorities / self.sum_tree.total()
+        weights = (self.size * sampling_probabilities) ** (-self.beta)
+        weights /= weights.max()  # Normalize for stability
+
+        data = SimpleNamespace(
+            observations=torch.from_numpy(self.observations[idxs]).to(self.device),
+            next_observations=torch.from_numpy(self.next_observations[idxs]).to(self.device),
+            actions=torch.from_numpy(self.actions[idxs]).to(self.device),
+            rewards=torch.from_numpy(self.rewards[idxs]).to(self.device),
+            dones=torch.from_numpy(self.dones[idxs]).to(self.device),
+        )
+        return data, idxs, weights
+
+    def update_priorities(self, idxs, errors):
+        for idx, error in zip(idxs, errors):
+            priority = (abs(error) + 1e-5) ** self.alpha
+            self.sum_tree.update(idx, priority)
+
+    def update_beta(self, fraction):
+        self.beta = (1.0 - self.beta_0) * fraction + self.beta_0
+
+
+class NoisyLinear(nn.Module):
+    def __init__(self, in_features, out_features, std_init=0.1):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.std_init = std_init
+
+        self.weight_mu = nn.Parameter(torch.Tensor(out_features, in_features))
+        self.weight_sigma = nn.Parameter(torch.Tensor(out_features, in_features))
+        self.register_buffer("weight_epsilon", torch.Tensor(out_features, in_features))
+
+        self.bias_mu = nn.Parameter(torch.Tensor(out_features))
+        self.bias_sigma = nn.Parameter(torch.Tensor(out_features))
+        self.register_buffer("bias_epsilon", torch.Tensor(out_features))
+
+        self.reset_parameters()
+        self.reset_noise()
+
+    def reset_parameters(self):
+        init.kaiming_uniform_(self.weight_mu, a=math.sqrt(5))
+        init.constant_(self.weight_sigma, self.std_init / math.sqrt(self.in_features))
+        init.constant_(self.bias_mu, 0)
+        init.constant_(self.bias_sigma, self.std_init / math.sqrt(self.out_features))
+
+    def reset_noise(self):
+        epsilon_in = self._scale_noise(self.in_features)
+        epsilon_out = self._scale_noise(self.out_features)
+        self.weight_epsilon.copy_(epsilon_out.outer(epsilon_in))
+        self.bias_epsilon.copy_(epsilon_out)
+
+    def _scale_noise(self, size):
+        x = torch.randn(size, device=self.weight_mu.device)
+        return x.sign().mul_(x.abs().sqrt_())
+
+    def forward(self, input):
+        weight = self.weight_mu + self.weight_sigma * self.weight_epsilon if self.training else self.weight_mu
+        bias = self.bias_mu + self.bias_sigma * self.bias_epsilon if self.training else self.bias_mu
+        return F.linear(input, weight, bias)
+
+
+# ALGO LOGIC: initialize agent here:
+class QNetwork(nn.Module):
+    def __init__(self, env, n_atoms=101, v_min=-100, v_max=100):
+        super().__init__()
+        self.env = env
+        self.n_atoms = n_atoms
+        self.register_buffer("atoms", torch.linspace(v_min, v_max, steps=n_atoms))
+        self.n = env.single_action_space.n
+
+        self.shared_layers = nn.Sequential(
+            nn.Conv2d(4, 32, 8, stride=4),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 4, stride=2),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, stride=1),
+            nn.ReLU(),
+            nn.Flatten(),
+        )
+        self.value_stream = nn.Sequential(NoisyLinear(3136, 512), nn.ReLU(), NoisyLinear(512, n_atoms))
+        self.advantage_stream = nn.Sequential(NoisyLinear(3136, 512), nn.ReLU(), NoisyLinear(512, self.n * n_atoms))
+
+    def reset_noise(self):
+        for module in self.modules():
+            if isinstance(module, NoisyLinear):
+                module.reset_noise()
+
+    def get_action(self, obs):
+        q_values_distributions = self.get_distribution(obs)
+        q_values = (torch.softmax(q_values_distributions, dim=2) * self.atoms).sum(2)
+        return torch.argmax(q_values, 1)
+
+    def get_distribution(self, obs):
+        x = self.shared_layers(obs / 255.0)
+        value = self.value_stream(x).view(-1, 1, self.n_atoms)
+        advantages = self.advantage_stream(x).view(-1, self.n, self.n_atoms)
+        return value + (advantages - advantages.mean(dim=1, keepdim=True))
+
+
+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" "gymnasium[atari,accept-rom-license]==0.28.1"  "ale-py==0.8.1" 
+"""
+        )
+    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 + i, 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"
+
+    q_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
+    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate, eps=0.01 / args.batch_size)
+    target_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
+    target_network.load_state_dict(q_network.state_dict())
+
+    rb = PrioritizedReplayBuffer(args.buffer_size, device)
+    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
+        actions = q_network.get_action(torch.Tensor(obs).to(device))
+        actions = actions.cpu().numpy()
+
+        # TRY NOT TO MODIFY: execute the game and log data.
+        next_obs, rewards, terminations, truncations, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                # Skip the envs that are not done
+                if "episode" not in info:
+                    continue
+                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()
+        for idx, trunc in enumerate(truncations):
+            if trunc:
+                real_next_obs[idx] = infos["final_observation"][idx]
+        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:
+            if global_step % args.train_frequency == 0:
+                data, idxs, weights = rb.sample(args.batch_size)
+
+                # Combine observations for a single network call
+                combined_obs = torch.cat([data.observations, data.next_observations], dim=0)
+                combined_dist = q_network.get_distribution(combined_obs)
+                dist, next_dist = combined_dist.split(len(data.observations), dim=0)
+
+                with torch.no_grad():
+                    next_q_values = (torch.softmax(next_dist, dim=2) * q_network.atoms).sum(2)
+                    next_actions = torch.argmax(next_q_values, 1)
+                    target_next_dist = target_network.get_distribution(data.next_observations)
+                    next_pmfs = torch.softmax(target_next_dist[torch.arange(len(data.next_observations)), next_actions], dim=1)
+                    next_atoms = data.rewards + args.gamma * target_network.atoms * (1 - data.dones.float())
+                    # projection
+                    delta_z = target_network.atoms[1] - target_network.atoms[0]
+                    tz = next_atoms.clamp(args.v_min, args.v_max)
+
+                    b = (tz - args.v_min) / delta_z
+                    l = b.floor().clamp(0, args.n_atoms - 1)
+                    u = b.ceil().clamp(0, args.n_atoms - 1)
+                    # (l == u).float() handles the case where bj is exactly an integer
+                    # example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
+                    d_m_l = (u + (l == u).float() - b) * next_pmfs
+                    d_m_u = (b - l) * next_pmfs
+                    target_pmfs = torch.zeros_like(next_pmfs)
+                    for i in range(target_pmfs.size(0)):
+                        target_pmfs[i].index_add_(0, l[i].long(), d_m_l[i])
+                        target_pmfs[i].index_add_(0, u[i].long(), d_m_u[i])
+
+                old_pmfs = torch.softmax(dist[torch.arange(len(data.observations)), data.actions.flatten()], dim=1)
+
+                expected_old_q = (old_pmfs.detach() * q_network.atoms).sum(-1)
+                expected_target_q = (target_pmfs * target_network.atoms).sum(-1)
+                td_error = expected_target_q - expected_old_q
+                rb.update_priorities(idxs, td_error.abs().cpu().numpy())
+                rb.update_beta(global_step / args.total_timesteps)
+
+                loss = (weights * -(target_pmfs * old_pmfs.clamp(min=1e-5, max=1 - 1e-5).log())).sum(-1).mean()
+
+                if global_step % 100 == 0:
+                    writer.add_scalar("losses/loss", loss.item(), global_step)
+                    writer.add_scalar("losses/q_values", expected_old_q.mean().item(), 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)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+                q_network.reset_noise()
+
+            # update target network
+            if global_step % args.target_network_frequency == 0:
+                target_network.load_state_dict(q_network.state_dict())
+
+    if args.save_model:
+        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
+        model_data = {
+            "model_weights": q_network.state_dict(),
+            "args": vars(args),
+        }
+        torch.save(model_data, model_path)
+        print(f"model saved to {model_path}")
+        from cleanrl_utils.evals.rainbow_eval import evaluate
+
+        episodic_returns = evaluate(
+            model_path,
+            make_env,
+            args.env_id,
+            eval_episodes=10,
+            run_name=f"{run_name}-eval",
+            Model=QNetwork,
+            device=device,
+        )
+        for idx, episodic_return in enumerate(episodic_returns):
+            writer.add_scalar("eval/episodic_return", episodic_return, idx)
+
+        if args.upload_model:
+            from cleanrl_utils.huggingface import push_to_hub
+
+            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
+            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
+            push_to_hub(args, episodic_returns, repo_id, "RAINBOW", f"runs/{run_name}", f"videos/{run_name}-eval")
+
+    envs.close()
+    writer.close()