[Examples] boiler plate code for multi-turn reward for RLHF

multi_turn_reward_for_RLHF/main.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+
+
+class DialogueEnv:
+    """Multi-turn dialogue environment that simulates conversations."""
+
+    def __init__(self):
+        self.turns = 5  # Each dialogue lasts 5 turns
+        self.current_turn = 0
+        self.conversation = []
+
+    def reset(self):
+        """Resets the environment for a new dialogue."""
+        self.current_turn = 0
+        self.conversation = []
+        return "Hi, how can I help you today?"  # Starting dialogue
+
+    def step(self, action):
+        """Takes an action (a response) and advances the conversation."""
+        self.conversation.append(action)
+        self.current_turn += 1
+
+        if self.current_turn < self.turns:
+            # Generate the next response from the environment (placeholder)
+            next_state = f"Response {self.current_turn}: How about this?"
+            done = False
+            reward = self._human_feedback(action)
+        else:
+            next_state = "Conversation ended."
+            done = True
+            reward = self._human_feedback(action)
+
+        return next_state, reward, done
+
+    def _human_feedback(self, action):
+        """Simulates human feedback by returning a random reward."""
+        return np.random.choice([1, -1])  # 1 for positive feedback, -1 for negative
+
+
+class PolicyNetwork(nn.Module):
+    """Policy network that defines the agent's behavior."""
+
+    def __init__(self, input_size=100, hidden_size=128, output_size=10):
+        super(PolicyNetwork, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        """Forward pass through the network."""
+        x = torch.relu(self.fc1(x))
+        return self.fc2(x)
+
+
+def pad_or_truncate(state, size=100):
+    """Pads or truncates the input state to match the required input size."""
+    state_tensor = torch.tensor([ord(c) for c in state], dtype=torch.float32)
+    if state_tensor.size(0) < size:
+        padded_tensor = torch.cat([state_tensor, torch.zeros(size - state_tensor.size(0))])
+    else:
+        padded_tensor = state_tensor[:size]
+    return padded_tensor.unsqueeze(0)  # Add batch dimension
+
+
+def train_rlhf(env, model, optimizer, num_episodes=1000):
+    """Trains the policy network using reinforcement learning with human feedback."""
+    gamma = 0.99  # Discount factor for future rewards
+
+    for episode in range(num_episodes):
+        state = env.reset()
+        total_reward = 0
+        log_probs = []
+        rewards = []
+
+        done = False
+        while not done:
+            # Pad or truncate the input state to the required size
+            state_tensor = pad_or_truncate(state, size=100)
+            logits = model(state_tensor)
+            action_probs = torch.softmax(logits, dim=-1)
+            action_dist = torch.distributions.Categorical(action_probs)
+
+            action = action_dist.sample()
+            log_prob = action_dist.log_prob(action)
+            log_probs.append(log_prob)
+
+            # Take the action in the environment
+            action_text = f"Action {action.item()}"
+            next_state, reward, done = env.step(action_text)
+            rewards.append(reward)
+            total_reward += reward
+
+            state = next_state
+
+        # Calculate the discounted rewards
+        discounted_rewards = []
+        cumulative_reward = 0
+        for r in reversed(rewards):
+            cumulative_reward = r + gamma * cumulative_reward
+            discounted_rewards.insert(0, cumulative_reward)
+
+        # Normalize the rewards
+        discounted_rewards = torch.tensor(discounted_rewards)
+        discounted_rewards = (discounted_rewards - discounted_rewards.mean()) / (discounted_rewards.std() + 1e-6)
+
+        # Policy Gradient: Update the policy
+        policy_loss = []
+        for log_prob, reward in zip(log_probs, discounted_rewards):
+            policy_loss.append(-log_prob * reward)
+
+        optimizer.zero_grad()
+        policy_loss = torch.cat(policy_loss).sum()
+        policy_loss.backward()
+        optimizer.step()
+
+        print(f"Episode {episode + 1}/{num_episodes}, Total Reward: {total_reward}")
+
+
+if __name__ == "__main__":
+    # Instantiate environment and model
+    env = DialogueEnv()
+    input_size = 100  # Placeholder for state size (e.g., fixed-length input of size 100)
+    hidden_size = 128
+    output_size = 10  # Placeholder for the number of possible actions (dialogue responses)
+
+    model = PolicyNetwork(input_size, hidden_size, output_size)
+    optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+    # Train the policy using RL with Human Feedback (simulated)
+    train_rlhf(env, model, optimizer, num_episodes=1000)