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TASK :fix below code 
thon.exe e:/LLMS/Fine-tuning/2024notes/RLNotes/example/trpo2.py
An error occurred: mat1 and mat2 shapes cannot be multiplied (1x128 and 4x2)


import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Categorical
from typing import Any, Callable, List, Tuple, Optional
import numpy as np
import gym
import copy
import warnings

# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")


class Actor(nn.Module):
    """
    Actor network for policy approximation.
    Accepts any PyTorch model architecture.
    """

    def __init__(self, base_model: nn.Module, action_space: gym.Space) -> None:
        """
        Initialize the Actor.

        Args:
            base_model (nn.Module): The base neural network model.
            action_space (gym.Space): The action space of the environment.
        """
        super(Actor, self).__init__()
        self.base = base_model
        if isinstance(action_space, gym.spaces.Discrete):
            # Ensure the base model has 'fc' attribute
            if hasattr(self.base, 'fc') and hasattr(self.base.fc, 'in_features'):
                self.action_head = nn.Linear(self.base.fc.in_features, action_space.n)
            else:
                raise AttributeError("The base model must have 'fc.in_features' for Discrete action spaces.")
        else:
            raise NotImplementedError("Only Discrete action spaces are supported.")

    def forward(self, x: torch.Tensor) -> Categorical:
        """
        Forward pass through the network.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            Categorical: Action distribution.
        """
        x = self.base(x)
        logits = self.action_head(x)
        return Categorical(logits=logits)


class Critic(nn.Module):
    """
    Critic network for value function approximation.
    Accepts any PyTorch model architecture.
    """

    def __init__(self, base_model: nn.Module) -> None:
        """
        Initialize the Critic.

        Args:
            base_model (nn.Module): The base neural network model.
        """
        super(Critic, self).__init__()
        self.base = base_model
        if hasattr(self.base, 'fc') and hasattr(self.base.fc, 'in_features'):
            self.value_head = nn.Linear(self.base.fc.in_features, 1)
        else:
            raise AttributeError("The base model must have 'fc.in_features' for Critic.")

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass through the network.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: State value.
        """
        x = self.base(x)
        value = self.value_head(x)
        return value


def conjugate_gradient(
    A: Callable[[torch.Tensor], torch.Tensor],
    b: torch.Tensor,
    cg_iters: int = 10,
    residual_tol: float = 1e-10
) -> torch.Tensor:
    """
    Conjugate gradient algorithm to solve Ax = b.

    Args:
        A (Callable[[torch.Tensor], torch.Tensor]): Function to compute the matrix-vector product.
        b (torch.Tensor): Right-hand side vector.
        cg_iters (int, optional): Maximum number of iterations. Defaults to 10.
        residual_tol (float, optional): Tolerance for convergence. Defaults to 1e-10.

    Returns:
        torch.Tensor: Solution vector x.
    """
    x = torch.zeros_like(b)
    r = b.clone()
    p = b.clone()
    rsold = torch.dot(r, r)

    for _ in range(cg_iters):
        Ap = A(p)
        pAp = torch.dot(p, Ap)
        if pAp == 0:
            break  # Prevent division by zero
        alpha = rsold / pAp
        x += alpha * p
        r -= alpha * Ap
        rsnew = torch.dot(r, r)
        if rsnew < residual_tol:
            break
        p = r + (rsnew / rsold) * p
        rsold = rsnew
    return x


def fisher_vector_product(
    kl: Callable[[torch.Tensor], torch.Tensor],
    params: List[torch.Tensor],
    vector: torch.Tensor,
    damping: float
) -> torch.Tensor:
    """
    Compute the Fisher-vector product.

    Args:
        kl (Callable[[torch.Tensor], torch.Tensor]): KL divergence function.
        params (List[torch.Tensor]): List of model parameters.
        vector (torch.Tensor): Vector to multiply with the Fisher Information Matrix.
        damping (float): Damping factor.

    Returns:
        torch.Tensor: Resulting tensor.
    """
    kl_grad = torch.autograd.grad(kl(), params, create_graph=True)
    flat_kl_grad = torch.cat([grad.view(-1) for grad in kl_grad])
    kl_v = (flat_kl_grad * vector).sum()
    kl_grad2 = torch.autograd.grad(kl_v, params, retain_graph=True)
    flat_kl_grad2 = torch.cat([g.contiguous().view(-1) for g in kl_grad2])
    return flat_kl_grad2 + damping * vector


class TRPOAgent:
    """
    Trust Region Policy Optimization (TRPO) agent.
    """

    def __init__(
        self,
        actor: Actor,
        critic: Critic,
        env: gym.Env,
        max_kl: float = 1e-2,
        damping: float = 1e-2,
        gamma: float = 0.99,
        lam: float = 0.95,
        cg_iters: int = 10,
        cg_tol: float = 1e-10
    ) -> None:
        """
        Initialize the TRPO agent.

        Args:
            actor (Actor): The actor network.
            critic (Critic): The critic network.
            env (gym.Env): The environment.
            max_kl (float, optional): Maximum KL divergence. Defaults to 1e-2.
            damping (float, optional): Damping factor for Fisher vector product. Defaults to 1e-2.
            gamma (float, optional): Discount factor. Defaults to 0.99.
            lam (float, optional): GAE lambda. Defaults to 0.95.
            cg_iters (int, optional): Conjugate gradient iterations. Defaults to 10.
            cg_tol (float, optional): Conjugate gradient tolerance. Defaults to 1e-10.
        """
        self.actor = actor
        self.critic = critic
        self.env = env
        self.max_kl = max_kl
        self.damping = damping
        self.gamma = gamma
        self.lam = lam
        self.cg_iters = cg_iters
        self.cg_tol = cg_tol

    def select_action(self, state: np.ndarray) -> Tuple[int, float]:
        """
        Select an action based on the current policy.

        Args:
            state (np.ndarray): The current state.

        Returns:
            Tuple[int, float]: The selected action and its log probability.
        """
        state = torch.from_numpy(state).float().unsqueeze(0)
        with torch.no_grad():
            dist = self.actor(state)
            action = dist.sample()
            log_prob = dist.log_prob(action)
        return action.item(), log_prob.item()

    def compute_advantages(
        self,
        rewards: List[float],
        masks: List[float],
        values: List[float]
    ) -> Tuple[List[float], List[float]]:
        """
        Compute advantages using Generalized Advantage Estimation (GAE).

        Args:
            rewards (List[float]): List of rewards.
            masks (List[float]): List indicating if the episode is done.
            values (List[float]): List of state values.

        Returns:
            Tuple[List[float], List[float]]: Advantages and returns.
        """
        advantages = []
        returns = []
        gae = 0
        for step in reversed(range(len(rewards))):
            delta = rewards[step] + self.gamma * values[step + 1] * masks[step] - values[step]
            gae = delta + self.gamma * self.lam * masks[step] * gae
            advantages.insert(0, gae)
            returns.insert(0, gae + values[step])
        return advantages, returns

    def update(self, trajectories: List[dict]) -> None:
        """
        Update the policy using TRPO.

        Args:
            trajectories (List[dict]): Collected trajectories.
        """
        # Flatten all trajectories
        all_steps = [t for traj in trajectories for t in traj["steps"]]
        states = torch.tensor(
            np.array([t["state"] for t in all_steps]),
            dtype=torch.float32
        )
        actions = torch.tensor(
            [t["action"] for t in all_steps],
            dtype=torch.int64
        )
        rewards = [t["reward"] for t in all_steps]
        masks = [1 - t["done"] for t in all_steps]

        # Compute values with bootstrap
        with torch.no_grad():
            values = self.critic(states).squeeze().numpy().tolist()
            last_value = self.critic(torch.from_numpy(np.array([all_steps[-1]["state"]], dtype=np.float32))).squeeze().item()
            values.append(last_value)  # bootstrap value

        advantages, returns = self.compute_advantages(rewards, masks, values)
        advantages = torch.tensor(advantages, dtype=torch.float32)
        returns = torch.tensor(returns, dtype=torch.float32)
        # Normalize advantages
        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)

        # Old policy
        old_dist = self.actor(states).log_prob(actions).detach()

        def compute_kl():
            new_dist = self.actor(states)
            kl_loss = (old_dist - new_dist.log_prob(actions)).mean()
            return kl_loss

        # Objective
        def get_loss():
            dist = self.actor(states)
            ratio = torch.exp(dist.log_prob(actions) - old_dist)
            return -(ratio * advantages).mean()

        loss = get_loss()

        # Compute gradient
        grads = torch.autograd.grad(loss, self.actor.parameters())
        grad = torch.cat([g.view(-1) for g in grads]).data

        # Compute step direction
        def fisher_vector_product_closure(v):
            return fisher_vector_product(compute_kl, list(self.actor.parameters()), v, self.damping)

        step_dir = conjugate_gradient(fisher_vector_product_closure, grad, self.cg_iters, self.cg_tol)

        shs = 0.5 * (step_dir * fisher_vector_product_closure(step_dir)).sum()
        step_size = torch.sqrt(2 * self.max_kl / shs)
        full_step = step_dir * step_size

        # Save old parameters
        old_params = torch.cat([p.data.view(-1) for p in self.actor.parameters()])

        # Perform line search
        def set_params(new_params):
            pointer = 0
            for p in self.actor.parameters():
                num = p.numel()
                p.data.copy_(new_params[pointer:pointer + num].view_as(p))
                pointer += num

        for _ in range(10):
            new_params = old_params + full_step
            set_params(new_params)
            new_loss = get_loss()
            kl = compute_kl()
            if new_loss < loss and kl < self.max_kl:
                break
            else:
                full_step *= 0.5
        else:
            set_params(old_params)

        # Update critic
        critic_loss = F.mse_loss(self.critic(states).squeeze(), returns)
        critic_optimizer = optim.Adam(self.critic.parameters(), lr=1e-3)
        critic_optimizer.zero_grad()
        critic_loss.backward()
        critic_optimizer.step()


def main() -> None:
    """
    Main function to train the TRPO agent.
    """
    try:
        env = gym.make("CartPole-v1")
        # Use a simple MLP for the actor and critic
        class SimpleModel(nn.Module):
            def __init__(self, input_dim: int, hidden_dim: int = 128) -> None:
                super(SimpleModel, self).__init__()
                self.fc = nn.Linear(input_dim, hidden_dim)
                self.relu = nn.ReLU()

            def forward(self, x: torch.Tensor) -> torch.Tensor:
                return self.relu(self.fc(x))

        input_dim = env.observation_space.shape[0]
        action_space = env.action_space

        base_actor = SimpleModel(input_dim)
        actor = Actor(base_actor, action_space)

        base_critic = SimpleModel(input_dim)
        critic = Critic(base_critic)

        agent = TRPOAgent(actor, critic, env)

        num_episodes = 100
        for episode in range(num_episodes):
            # Handle env.reset() for different Gym versions
            reset_output = env.reset()
            if isinstance(reset_output, tuple):
                state, _ = reset_output
            else:
                state = reset_output

            trajectories = []
            done = False
            while not done:
                action, log_prob = agent.select_action(state)
                step_output = env.step(action)

                # Handle env.step() for different Gym versions
                if isinstance(step_output, tuple) and len(step_output) == 5:
                    next_state, reward, terminated, truncated, info = step_output
                    done = terminated or truncated
                elif isinstance(step_output, tuple) and len(step_output) == 4:
                    next_state, reward, done, info = step_output
                else:
                    raise ValueError("Unexpected number of outputs from env.step()")

                trajectories.append({
                    "state": state,
                    "action": action,
                    "reward": reward,
                    "done": done
                })
                state = next_state

            agent.update([{"steps": trajectories}])
            total_reward = sum(t["reward"] for t in trajectories)
            print(f"Episode {episode + 1}: Total Reward: {total_reward}")
    except Exception as e:
        print(f"An error occurred: {e}")


if __name__ == "__main__":
    main()