### Reinforce Algorithm Code Implementation In reinforcement learning, the **Reinforce algorithm**, also known as Monte Carlo Policy Gradient, provides a method for optimizing policy directly by adjusting parameters based on sampled trajectories. The core idea involves estimating gradients using samples from the environment and updating policies accordingly. The following Python code demonstrates a simple implementation of the Reinforce algorithm: ```python import numpy as np import torch import torch.nn as nn import torch.optim as optim class PolicyNetwork(nn.Module): def __init__(self, state_size, action_size, hidden_size=128): super(PolicyNetwork, self).__init__() self.fc1 = nn.Linear(state_size, hidden_size) self.fc2 = nn.Linear(hidden_size, action_size) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return torch.softmax(x, dim=-1) def reinforce(policy_net, optimizer, episodes, env, gamma=0.99): episode_rewards = [] for _ in range(episodes): log_probs = [] rewards = [] state = env.reset() done = False while not done: state_tensor = torch.FloatTensor([state]) action_probabilities = policy_net(state_tensor) distribution = torch.distributions.Categorical(action_probabilities) action = distribution.sample() next_state, reward, done, _ = env.step(action.item()) log_probs.append(distribution.log_prob(action)) rewards.append(reward) state = next_state discounted_rewards = calculate_discounted_rewards(rewards, gamma) normalized_rewards = (discounted_rewards - np.mean(discounted_rewards)) / ( np.std(discounted_rewards) + 1e-9) loss = [] for log_prob, R in zip(log_probs, normalized_rewards): loss.append(-log_prob * R) optimizer.zero_grad() total_loss = torch.stack(loss).sum() total_loss.backward() optimizer.step() episode_rewards.append(sum(rewards)) return episode_rewards def calculate_discounted_rewards(rewards, gamma): discounted_r = np.zeros_like(rewards) running_add = 0 for t in reversed(range(len(rewards))): running_add = running_add * gamma + rewards[t] discounted_r[t] = running_add return discounted_r ``` This script defines a neural network-based policy function approximator along with functions to perform updates according to the REINFORCE rule[^1].