# /// script # requires-python = ">=3.12" # dependencies = [ # "gymnasium[classic-control]", # "marimo", # "matplotlib", # "moviepy", # "numpy", # "torch", # ] # /// import marimo __generated_with = "0.21.0" app = marimo.App(width="full") @app.cell def _(): import marimo as mo return (mo,) @app.cell(hide_code=True) def _(mo): mo.md(r""" # 08. Rainbow [M. Hessel et al., "Rainbow: Combining Improvements in Deep Reinforcement Learning." arXiv preprint arXiv:1710.02298, 2017.](https://arxiv.org/pdf/1710.02298.pdf) We will integrate all the following seven components into a single integrated agent, which is called Rainbow! 1. DQN 2. Double DQN 3. Prioritized Experience Replay 4. Dueling Network 5. Noisy Network 6. Categorical DQN 7. N-step Learning This method shows an impressive performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. ![rainbow](https://user-images.githubusercontent.com/14961526/60591412-61748100-9dd9-11e9-84fb-076c7a61fbab.png) However, the integration is not so simple because some of components are not independent each other, so we will look into a number of points that people especailly feel confused. 1. Noisy Network <-> Dueling Network 2. Dueling Network <-> Categorical DQN 3. Categorical DQN <-> Double DQN """) return @app.cell def _(): import math import os import random import warnings from collections import deque import gymnasium as gym import matplotlib.pyplot as plt import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.nn.utils import clip_grad_norm_ return ( F, clip_grad_norm_, deque, gym, math, nn, np, optim, os, plt, random, torch, warnings, ) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Replay buffer Same as the basic N-step buffer. (Please see *01_dqn.py*, *07_n_step_learning.py* for detailed description about the basic (n-step) replay buffer.) """) return @app.cell def _(deque, np): class ReplayBuffer: """A simple numpy replay buffer.""" def __init__( self, obs_dim: int, size: int, batch_size: int = 32, n_step: int = 1, gamma: float = 0.99, ): self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32) self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32) self.acts_buf = np.zeros([size], dtype=np.float32) self.rews_buf = np.zeros([size], dtype=np.float32) self.terminated_buf = np.zeros(size, dtype=np.float32) self.max_size, self.batch_size = size, batch_size ( self.ptr, self.size, ) = 0, 0 # for N-step Learning self.n_step_buffer = deque(maxlen=n_step) self.n_step = n_step self.gamma = gamma def store( self, obs: np.ndarray, act: np.ndarray, rew: float, next_obs: np.ndarray, terminated: bool, ) -> tuple[np.ndarray, np.ndarray, float, np.ndarray, bool]: transition = (obs, act, rew, next_obs, terminated) self.n_step_buffer.append(transition) # single step transition is not ready if len(self.n_step_buffer) < self.n_step: return () # make a n-step transition rew, next_obs, terminated = self._get_n_step_info(self.n_step_buffer, self.gamma) obs, act = self.n_step_buffer[0][:2] self.obs_buf[self.ptr] = obs self.next_obs_buf[self.ptr] = next_obs self.acts_buf[self.ptr] = act self.rews_buf[self.ptr] = rew self.terminated_buf[self.ptr] = terminated self.ptr = (self.ptr + 1) % self.max_size self.size = min(self.size + 1, self.max_size) return self.n_step_buffer[0] def sample_batch(self) -> dict[str, np.ndarray]: idxs = np.random.choice(self.size, size=self.batch_size, replace=False) return dict( obs=self.obs_buf[idxs], next_obs=self.next_obs_buf[idxs], acts=self.acts_buf[idxs], rews=self.rews_buf[idxs], terminated=self.terminated_buf[idxs], # for N-step Learning indices=idxs, ) def sample_batch_from_idxs(self, idxs: np.ndarray) -> dict[str, np.ndarray]: # for N-step Learning return dict( obs=self.obs_buf[idxs], next_obs=self.next_obs_buf[idxs], acts=self.acts_buf[idxs], rews=self.rews_buf[idxs], terminated=self.terminated_buf[idxs], ) def _get_n_step_info( self, n_step_buffer: deque, gamma: float ) -> tuple[np.int64, np.ndarray, bool]: """Return n step rew, next_obs, and terminated.""" # info of the last transition rew, next_obs, terminated = n_step_buffer[-1][-3:] for transition in reversed(list(n_step_buffer)[:-1]): r, n_o, d = transition[-3:] rew = r + gamma * rew * (1 - d) next_obs, terminated = (n_o, d) if d else (next_obs, terminated) return rew, next_obs, terminated def __len__(self) -> int: return self.size return (ReplayBuffer,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Segment Tree Same segment tree implementation used in *03_per.py*. See that notebook for a detailed explanation of the data structure. - **SegmentTree**: Base class supporting any associative binary operation with O(log n) updates and range queries. - **SumSegmentTree**: Tracks cumulative priorities for proportional sampling. - **MinSegmentTree**: Tracks minimum priority for importance-sampling weight normalization. """) return @app.cell def _(): import operator from collections.abc import Callable class SegmentTree: """Create SegmentTree. Taken from OpenAI baselines github repository: https://github.com/openai/baselines/blob/master/baselines/common/segment_tree.py """ def __init__(self, capacity: int, operation: Callable, init_value: float): assert capacity > 0 and capacity & (capacity - 1) == 0, ( "capacity must be positive and a power of 2." ) self.capacity = capacity self.tree = [init_value for _ in range(2 * capacity)] self.operation = operation def _operate_helper( self, start: int, end: int, node: int, node_start: int, node_end: int ) -> float: if start == node_start and end == node_end: return self.tree[node] mid = (node_start + node_end) // 2 if end <= mid: return self._operate_helper(start, end, 2 * node, node_start, mid) elif mid + 1 <= start: return self._operate_helper(start, end, 2 * node + 1, mid + 1, node_end) else: return self.operation( self._operate_helper(start, mid, 2 * node, node_start, mid), self._operate_helper(mid + 1, end, 2 * node + 1, mid + 1, node_end), ) def operate(self, start: int = 0, end: int = 0) -> float: if end <= 0: end += self.capacity end -= 1 return self._operate_helper(start, end, 1, 0, self.capacity - 1) def __setitem__(self, idx: int, val: float): idx += self.capacity self.tree[idx] = val idx //= 2 while idx >= 1: self.tree[idx] = self.operation(self.tree[2 * idx], self.tree[2 * idx + 1]) idx //= 2 def __getitem__(self, idx: int) -> float: assert 0 <= idx < self.capacity return self.tree[self.capacity + idx] class SumSegmentTree(SegmentTree): def __init__(self, capacity: int): super().__init__(capacity=capacity, operation=operator.add, init_value=0.0) def sum(self, start: int = 0, end: int = 0) -> float: return super().operate(start, end) def retrieve(self, upperbound: float) -> int: assert 0 <= upperbound <= self.sum() + 1e-5, f"upperbound: {upperbound}" idx = 1 while idx < self.capacity: left = 2 * idx right = left + 1 if self.tree[left] > upperbound: idx = 2 * idx else: upperbound -= self.tree[left] idx = right return idx - self.capacity class MinSegmentTree(SegmentTree): def __init__(self, capacity: int): super().__init__(capacity=capacity, operation=min, init_value=float("inf")) def min(self, start: int = 0, end: int = 0) -> float: return super().operate(start, end) return MinSegmentTree, SumSegmentTree @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Prioritized Replay Buffer Combines PER with N-step learning. The `store` method returns a tuple to indicate whether an N-step transition has been completed — only then is it added to the priority buffer. (Please see *03_per.py* for detailed description about PER.) """) return @app.cell def _(MinSegmentTree, ReplayBuffer, SumSegmentTree, np, random): class PrioritizedReplayBuffer(ReplayBuffer): """Prioritized Replay buffer. Attributes: max_priority (float): max priority tree_ptr (int): next index of tree alpha (float): alpha parameter for prioritized replay buffer sum_tree (SumSegmentTree): sum tree for prior min_tree (MinSegmentTree): min tree for min prior to get max weight """ def __init__( self, obs_dim: int, size: int, batch_size: int = 32, alpha: float = 0.6, n_step: int = 1, gamma: float = 0.99, ): """Initialization.""" assert alpha >= 0 super(PrioritizedReplayBuffer, self).__init__(obs_dim, size, batch_size, n_step, gamma) self.max_priority, self.tree_ptr = 1.0, 0 self.alpha = alpha # capacity must be positive and a power of 2. tree_capacity = 1 while tree_capacity < self.max_size: tree_capacity *= 2 self.sum_tree = SumSegmentTree(tree_capacity) self.min_tree = MinSegmentTree(tree_capacity) def store( self, obs: np.ndarray, act: int, rew: float, next_obs: np.ndarray, terminated: bool, ) -> tuple[np.ndarray, np.ndarray, float, np.ndarray, bool]: """Store experience and priority.""" transition = super().store(obs, act, rew, next_obs, terminated) if transition: self.sum_tree[self.tree_ptr] = self.max_priority**self.alpha self.min_tree[self.tree_ptr] = self.max_priority**self.alpha self.tree_ptr = (self.tree_ptr + 1) % self.max_size return transition def sample_batch(self, beta: float = 0.4) -> dict[str, np.ndarray]: """Sample a batch of experiences.""" assert len(self) >= self.batch_size assert beta > 0 indices = self._sample_proportional() obs = self.obs_buf[indices] next_obs = self.next_obs_buf[indices] acts = self.acts_buf[indices] rews = self.rews_buf[indices] terminated = self.terminated_buf[indices] weights = np.array([self._calculate_weight(i, beta) for i in indices]) return dict( obs=obs, next_obs=next_obs, acts=acts, rews=rews, terminated=terminated, weights=weights, indices=indices, ) def update_priorities(self, indices: list[int], priorities: np.ndarray): """Update priorities of sampled transitions.""" assert len(indices) == len(priorities) for idx, priority in zip(indices, priorities): assert priority > 0 assert 0 <= idx < len(self) self.sum_tree[idx] = priority**self.alpha self.min_tree[idx] = priority**self.alpha self.max_priority = max(self.max_priority, priority) def _sample_proportional(self) -> list[int]: """Sample indices based on proportions.""" indices = [] p_total = self.sum_tree.sum(0, len(self)) segment = p_total / self.batch_size for i in range(self.batch_size): a = segment * i b = segment * (i + 1) upperbound = random.uniform(a, b) idx = self.sum_tree.retrieve(upperbound) indices.append(idx) return indices def _calculate_weight(self, idx: int, beta: float): """Calculate the weight of the experience at idx.""" # get max weight p_min = self.min_tree.min() / self.sum_tree.sum() max_weight = (p_min * len(self)) ** (-beta) # calculate weights p_sample = self.sum_tree[idx] / self.sum_tree.sum() weight = (p_sample * len(self)) ** (-beta) weight = weight / max_weight return weight return (PrioritizedReplayBuffer,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Noisy Layer Please see *05_noisy_net.py* for detailed description. **References:** - https://github.com/higgsfield/RL-Adventure/blob/master/5.noisy%20dqn.ipynb - https://github.com/Kaixhin/Rainbow/blob/master/model.py """) return @app.cell def _(F, math, nn, torch): class NoisyLinear(nn.Module): """Noisy linear module for NoisyNet. Attributes: in_features (int): input size of linear module out_features (int): output size of linear module std_init (float): initial std value weight_mu (nn.Parameter): mean value weight parameter weight_sigma (nn.Parameter): std value weight parameter bias_mu (nn.Parameter): mean value bias parameter bias_sigma (nn.Parameter): std value bias parameter """ def __init__( self, in_features: int, out_features: int, std_init: float = 0.5, ): """Initialization.""" super(NoisyLinear, self).__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): """Reset trainable network parameters (factorized gaussian noise).""" mu_range = 1 / math.sqrt(self.in_features) self.weight_mu.data.uniform_(-mu_range, mu_range) self.weight_sigma.data.fill_(self.std_init / math.sqrt(self.in_features)) self.bias_mu.data.uniform_(-mu_range, mu_range) self.bias_sigma.data.fill_(self.std_init / math.sqrt(self.in_features)) def reset_noise(self): """Make new noise.""" epsilon_in = self.scale_noise(self.in_features) epsilon_out = self.scale_noise(self.out_features) # outer product self.weight_epsilon.copy_(epsilon_out.ger(epsilon_in)) self.bias_epsilon.copy_(epsilon_out) def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward method implementation. In eval mode, use only the mean weights (no noise) for deterministic action selection, following Google Dopamine. """ if self.training: return F.linear( x, self.weight_mu + self.weight_sigma * self.weight_epsilon, self.bias_mu + self.bias_sigma * self.bias_epsilon, ) return F.linear(x, self.weight_mu, self.bias_mu) @staticmethod def scale_noise(size: int) -> torch.Tensor: """Set scale to make noise (factorized gaussian noise).""" x = torch.randn(size) return x.sign().mul(x.abs().sqrt()) return (NoisyLinear,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## NoisyNet + DuelingNet + Categorical DQN #### NoisyNet + DuelingNet NoisyLinear is employed for the last two layers of advantage and value layers. The noise should be reset at evey update step. #### DuelingNet + Categorical DQN The dueling network architecture is adapted for use with return distributions. The network has a shared representation, which is then fed into a value stream with atom_size outputs, and into an advantage stream with atom_size × out_dim outputs. For each atom, the value and advantage streams are aggregated, as in dueling DQN, and then passed through a softmax layer to obtain the normalized parametric distributions used to estimate the returns’ distributions. ``` advantage = self.advantage_layer(adv_hid).view(-1, self.out_dim, self.atom_size) value = self.value_layer(val_hid).view(-1, 1, self.atom_size) q_atoms = value + advantage - advantage.mean(dim=1, keepdim=True) dist = F.softmax(q_atoms, dim=-1) ``` (Please see *04_dueling.py*, *05_noisy_net.py*, *06_categorical_dqn.py* for detailed description of each component's network architecture.) """) return @app.cell def _(F, NoisyLinear, nn, torch): class Network(nn.Module): def __init__(self, in_dim: int, out_dim: int, atom_size: int, support: torch.Tensor): """Initialization.""" super(Network, self).__init__() self.support = support self.out_dim = out_dim self.atom_size = atom_size # set common feature layer self.feature_layer = nn.Sequential( nn.Linear(in_dim, 128), nn.ReLU(), ) # set advantage layer self.advantage_hidden_layer = NoisyLinear(128, 128) self.advantage_layer = NoisyLinear(128, out_dim * atom_size) # set value layer self.value_hidden_layer = NoisyLinear(128, 128) self.value_layer = NoisyLinear(128, atom_size) def forward(self, x: torch.Tensor) -> torch.Tensor: """Forward method implementation.""" dist = self.dist(x) q = torch.sum(dist * self.support, dim=2) return q def _logits(self, x: torch.Tensor) -> torch.Tensor: """Get raw logits for atom distributions.""" feature = self.feature_layer(x) adv_hid = F.relu(self.advantage_hidden_layer(feature)) val_hid = F.relu(self.value_hidden_layer(feature)) advantage = self.advantage_layer(adv_hid).view(-1, self.out_dim, self.atom_size) value = self.value_layer(val_hid).view(-1, 1, self.atom_size) q_atoms = value + advantage - advantage.mean(dim=1, keepdim=True) return q_atoms def dist(self, x: torch.Tensor) -> torch.Tensor: """Get distribution for atoms.""" return F.softmax(self._logits(x), dim=-1) def log_dist(self, x: torch.Tensor) -> torch.Tensor: """Get log-distribution for atoms (numerically stable).""" return F.log_softmax(self._logits(x), dim=-1) def reset_noise(self): """Reset all noisy layers.""" self.advantage_hidden_layer.reset_noise() self.advantage_layer.reset_noise() self.value_hidden_layer.reset_noise() self.value_layer.reset_noise() return (Network,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Rainbow Agent Here is a summary of DQNAgent class. | Method | Note | | --- | --- | |select_action | select an action from the input state. | |step | take an action and return the response of the env. | |compute_dqn_loss | return dqn loss. | |update_model | update the model by gradient descent. | |target_hard_update| hard update from the local model to the target model.| |train | train the agent during num_frames. | |test | test the agent (1 episode). | |plot | plot the training progresses. | #### Categorical DQN + Double DQN The idea of Double Q-learning is to reduce overestimations by decomposing the max operation in the target into action selection and action evaluation. Here, we use `self.dqn` instead of `self.dqn_target` to obtain the target actions. ``` # Categorical DQN + Double DQN # target_dqn is used when we don't employ double DQN next_action = self.dqn(next_state).argmax(1) next_dist = self.dqn_target.dist(next_state) next_dist = next_dist[range(self.batch_size), next_action] ``` """) return @app.cell def _( Network, PrioritizedReplayBuffer, ReplayBuffer, clip_grad_norm_, gym, mo, np, optim, plt, torch, warnings, ): class DQNAgent: """DQN Agent interacting with environment. Attribute: env (gym.Env): openAI Gym environment memory (PrioritizedReplayBuffer): replay memory to store transitions batch_size (int): batch size for sampling target_update (int): period for target model's hard update gamma (float): discount factor dqn (Network): model to train and select actions dqn_target (Network): target model to update optimizer (torch.optim): optimizer for training dqn transition (list): transition information including state, action, reward, next_state, done v_min (float): min value of support v_max (float): max value of support atom_size (int): the unit number of support support (torch.Tensor): support for categorical dqn use_n_step (bool): whether to use n_step memory n_step (int): step number to calculate n-step td error memory_n (ReplayBuffer): n-step replay buffer """ def __init__( self, env: gym.Env, memory_size: int, batch_size: int, target_update: int, seed: int, gamma: float = 0.99, # PER parameters alpha: float = 0.2, beta: float = 0.6, prior_eps: float = 1e-6, # Categorical DQN parameters v_min: float = 0.0, v_max: float = 200.0, atom_size: int = 51, # N-step Learning n_step: int = 3, ): """Initialization. Args: env (gym.Env): openAI Gym environment memory_size (int): length of memory batch_size (int): batch size for sampling target_update (int): period for target model's hard update lr (float): learning rate gamma (float): discount factor alpha (float): determines how much prioritization is used beta (float): determines how much importance sampling is used prior_eps (float): guarantees every transition can be sampled v_min (float): min value of support v_max (float): max value of support atom_size (int): the unit number of support n_step (int): step number to calculate n-step td error """ obs_dim = env.observation_space.shape[0] action_dim = env.action_space.n self.env = env self.batch_size = batch_size self.target_update = target_update self.seed = seed self.gamma = gamma # NoisyNet: All attributes related to epsilon are removed # device: cpu / gpu self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(self.device) # PER # memory for 1-step Learning self.beta = beta self.beta_initial = beta self.prior_eps = prior_eps self.memory = PrioritizedReplayBuffer( obs_dim, memory_size, batch_size, alpha=alpha, gamma=gamma ) # memory for N-step Learning self.use_n_step = True if n_step > 1 else False if self.use_n_step: self.n_step = n_step self.memory_n = ReplayBuffer( obs_dim, memory_size, batch_size, n_step=n_step, gamma=gamma ) # Categorical DQN parameters self.v_min = v_min self.v_max = v_max self.atom_size = atom_size self.support = torch.linspace(self.v_min, self.v_max, self.atom_size).to(self.device) # networks: dqn, dqn_target self.dqn = Network(obs_dim, action_dim, self.atom_size, self.support).to(self.device) self.dqn_target = Network(obs_dim, action_dim, self.atom_size, self.support).to( self.device ) self.dqn_target.load_state_dict(self.dqn.state_dict()) self.dqn_target.eval() # optimizer self.optimizer = optim.Adam(self.dqn.parameters()) # transition to store in memory self.transition = list() # mode: train / test self.is_test = False def select_action(self, state: np.ndarray) -> np.ndarray: """Select an action from the input state.""" # NoisyNet: no epsilon greedy action selection # Disable noise during test for deterministic evaluation if self.is_test: self.dqn.eval() selected_action = self.dqn(torch.FloatTensor(state).to(self.device)).argmax() selected_action = selected_action.detach().cpu().numpy() if not self.is_test: self.transition = [state, selected_action] return selected_action def step(self, action: np.ndarray) -> tuple[np.ndarray, np.float64, bool]: """Take an action and return the response of the env.""" next_state, reward, terminated, truncated, _ = self.env.step(action) done = terminated or truncated if not self.is_test: self.transition += [reward, next_state, terminated] # N-step transition if self.use_n_step: one_step_transition = self.memory_n.store(*self.transition) # 1-step transition else: one_step_transition = self.transition # add a single step transition if one_step_transition: self.memory.store(*one_step_transition) return next_state, reward, done def update_model(self) -> torch.Tensor: """Update the model by gradient descent.""" # PER needs beta to calculate weights samples = self.memory.sample_batch(self.beta) weights = torch.FloatTensor(samples["weights"]).to(self.device) indices = samples["indices"] # 1-step Learning loss elementwise_loss = self._compute_dqn_loss(samples, self.gamma) # PER: importance sampling before average loss = torch.mean(elementwise_loss * weights) # N-step Learning loss # we are gonna combine 1-step loss and n-step loss so as to # prevent high-variance. The original rainbow employs n-step loss only. if self.use_n_step: gamma = self.gamma**self.n_step samples = self.memory_n.sample_batch_from_idxs(indices) elementwise_loss_n_loss = self._compute_dqn_loss(samples, gamma) elementwise_loss += elementwise_loss_n_loss # PER: importance sampling before average loss = torch.mean(elementwise_loss * weights) self.optimizer.zero_grad() loss.backward() clip_grad_norm_(self.dqn.parameters(), 10.0) self.optimizer.step() # PER: update priorities loss_for_prior = elementwise_loss.detach().cpu().numpy() new_priorities = loss_for_prior + self.prior_eps self.memory.update_priorities(indices, new_priorities) # NoisyNet: reset noise self.dqn.reset_noise() self.dqn_target.reset_noise() return loss.item() def train(self, num_frames: int, plotting_interval: int = 200): """Train the agent.""" self.is_test = False state, _ = self.env.reset(seed=self.seed) update_cnt = 0 losses = [] scores = [] score = 0 for frame_idx in range(1, num_frames + 1): action = self.select_action(state) next_state, reward, done = self.step(action) state = next_state score += reward # NoisyNet: removed decrease of epsilon # PER: increase beta fraction = min(frame_idx / num_frames, 1.0) self.beta = self.beta_initial + fraction * (1.0 - self.beta_initial) # if episode ends if done: state, _ = self.env.reset(seed=self.seed) scores.append(score) score = 0 # if training is ready if len(self.memory) >= self.batch_size: loss = self.update_model() losses.append(loss) update_cnt += 1 # if hard update is needed if update_cnt % self.target_update == 0: self._target_hard_update() # plotting if frame_idx % plotting_interval == 0: self._plot(frame_idx, scores, losses) self.env.close() def test(self, video_folder: str) -> None: """Test the agent.""" self.is_test = True # for recording a video naive_env = self.env with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) self.env = gym.wrappers.RecordVideo(self.env, video_folder=video_folder) state, _ = self.env.reset(seed=self.seed) done = False score = 0 while not done: action = self.select_action(state) next_state, reward, done = self.step(action) state = next_state score += reward self.env.close() # reset self.env = naive_env self.dqn.train() return score def _compute_dqn_loss(self, samples: dict[str, np.ndarray], gamma: float) -> torch.Tensor: """Return categorical dqn loss.""" device = self.device # for shortening the following lines state = torch.FloatTensor(samples["obs"]).to(device) next_state = torch.FloatTensor(samples["next_obs"]).to(device) action = torch.LongTensor(samples["acts"]).to(device) reward = torch.FloatTensor(samples["rews"].reshape(-1, 1)).to(device) terminated = torch.FloatTensor(samples["terminated"].reshape(-1, 1)).to(device) # Categorical DQN algorithm delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1) with torch.no_grad(): # Double DQN next_action = self.dqn(next_state).argmax(1) next_dist = self.dqn_target.dist(next_state) next_dist = next_dist[range(self.batch_size), next_action] t_z = reward + (1 - terminated) * gamma * self.support t_z = t_z.clamp(min=self.v_min, max=self.v_max) b = (t_z - self.v_min) / delta_z l = b.floor().long() u = b.floor().long() + 1 offset = ( torch.linspace(0, (self.batch_size - 1) * self.atom_size, self.batch_size) .long() .unsqueeze(1) .expand(self.batch_size, self.atom_size) .to(self.device) ) proj_dist = torch.zeros(next_dist.size(), device=self.device) proj_dist.view(-1).index_add_( 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1) ) proj_dist.view(-1).index_add_( 0, (u.clamp(max=self.atom_size - 1) + offset).view(-1), (next_dist * (b - l.float())).view(-1), ) log_dist = self.dqn.log_dist(state) log_p = log_dist[range(self.batch_size), action] elementwise_loss = -(proj_dist * log_p).sum(1) return elementwise_loss def _target_hard_update(self): """Hard update: target <- local.""" self.dqn_target.load_state_dict(self.dqn.state_dict()) def _plot( self, frame_idx: int, scores: list[float], losses: list[float], ): """Plot the training progresses.""" plt.close("all") plt.figure(figsize=(20, 5)) plt.subplot(131) plt.title("frame %s. score: %s" % (frame_idx, np.mean(scores[-10:]))) plt.plot(scores) plt.subplot(132) plt.title("loss") plt.plot(losses) mo.output.replace(mo.as_html(plt.gcf())) return (DQNAgent,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Environment You can see the [code](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/classic_control/cartpole.py) and [configurations](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/classic_control/cartpole.py#L91) of CartPole-v1 from Farama Gymnasium's repository. """) return @app.cell def _(gym): # environment env = gym.make("CartPole-v1", max_episode_steps=200, render_mode="rgb_array") return (env,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Set random seed """) return @app.cell def _(np, random, torch): seed = 777 def seed_torch(seed): torch.manual_seed(seed) if torch.backends.cudnn.enabled: torch.cuda.manual_seed(seed) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True np.random.seed(seed) random.seed(seed) seed_torch(seed) return (seed,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Initialize """) return @app.cell def _(DQNAgent, env, seed): # parameters num_frames = 20000 memory_size = 5000 batch_size = 32 target_update = 100 # train agent = DQNAgent(env, memory_size, batch_size, target_update, seed) return agent, num_frames @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Train """) return @app.cell def _(agent, num_frames): agent.train(num_frames) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Test Run the trained agent (1 episode). """) return @app.cell def _(agent, mo): video_folder = "videos/rainbow" score = agent.test(video_folder=video_folder) mo.output.replace(mo.md(f"**Test score: {score}**")) return (video_folder,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ## Render """) return @app.cell def _(mo, os, video_folder): import glob def show_latest_video(video_folder: str): list_of_files = glob.glob(os.path.join(video_folder, "*.mp4")) latest_file = max(list_of_files, key=os.path.getctime) return latest_file latest_file = show_latest_video(video_folder=video_folder) mo.output.replace(mo.video(src=open(latest_file, "rb").read())) return if __name__ == "__main__": app.run()