In my reinforcement learning algorithm, I implemented the following logic in coding(I donot know whether my implemented logic is correct or not related to intermediate reward processing), i passed the intermediate reward to the agent, wen we get 'reward = -200' or 'reward >= 90'. 

Previously My code was implemented by passing the reward function(append reward) to the agent after completing the maximum step size of the iteration loop in that current episode at the end of each episode, 
but, later i modified my code by pass the intermediate reward(while drastic deviation occurs) with in the episode to agent to guide it more effectively. So, i updated the previous code based on my requirement which is given below(my modified code is given below for your review).

I donot know whether the logic or the formal procedure which i implemented for passing the intermediate reward to the PPO agent is correct or wrong, because i am passing the data which are corresponding to that timestep where the intermediate reward occurs to the PPO agent to update policy intermediately, and also i am passing all the datas including the data which were passed intermediately at the end os each episode for final policy update. please rewiew the code completely and advice me the potential issue in my logic, whether the intermediate policy updation were properly synchronized and logically correct with the reinforcement algorithm procesures and policy updation procedures.

# PPOAgent definition
class PPOAgent:
    def __init__(self, state_dim, action_space, lr_actor, lr_critic, gamma, gae_lambda, epsilon, policy_clip, epochs, entropy_coef):
        # Instantiation

    def select_action(self, state, performance_metrics):               
	# Existing Code
        return rounded_actions, log_probs.detach(), performance_metrics

    def compute_gae(self, next_value, rewards, masks, values):
        returns = []
        gae = 0
        for step in reversed(range(len(rewards))):
            delta = rewards[step] + self.gamma * next_value * masks[step] - values[step]
            gae = delta + self.gamma * self.gae_lambda * masks[step] * gae
            returns.insert(0, gae + values[step])
            next_value = values[step]
        return returns

    def update_policy(self, prev_states, prev_actions, prev_log_probs, returns, advantages):
        device = next(self.actor.parameters()).device  
        advantages = torch.tensor(advantages, dtype=torch.float32, device=device)
        returns = torch.tensor(returns, dtype=torch.float32, device=device)
        prev_log_probs = torch.tensor(prev_log_probs, dtype=torch.float32, device=device)        
        
        # Initialize lists to accumulate losses
        actor_losses = []
        critic_losses = []
        
        for _ in range(self.epochs):
            log_probs_batch, state_values_batch, entropies_batch = [], [], []
            for state, action in zip(prev_states, prev_actions):
                node_features_tensor, edge_index = state
                node_features_tensor = node_features_tensor.to(device)
                edge_index = edge_index.to(device) if edge_index is not None else None
                
                action_means, action_stds = self.actor(state)
                
                # Getting the state value prediction from the critic using the flattened state
                state_value = self.critic(node_features_tensor, edge_index).unsqueeze(0)
                
                dist = torch.distributions.Normal(action_means, action_stds)
                action_tensor = torch.tensor(action, dtype=torch.float32, device=action_means.device)
                log_prob = dist.log_prob(action_tensor).sum(axis=-1).unsqueeze(0)                
                log_probs_batch.append(log_prob)
                
                entropy = dist.entropy().sum(axis=-1).unsqueeze(0)
                entropies_batch.append(entropy)
                
                state_values_batch.append(state_value)
             
            # Convert to tensors
            log_probs = torch.cat(log_probs_batch).to(prev_log_probs.device)
            state_values = torch.cat(state_values_batch).squeeze(-1).to(returns.device) 
            entropy = torch.cat(entropies_batch)
            
            # Surrogate function
            ratios = torch.exp(log_probs - prev_log_probs)            
            surr1 = ratios * advantages
            surr2 = torch.clamp(ratios, 1.0 - self.policy_clip, 1.0 + self.policy_clip) * advantages
                        
            actor_loss = - torch.min(surr1, surr2).mean() - self.entropy_coef * entropy.mean()
            critic_loss = F.mse_loss(state_values, returns)
            
            # Update actor
            self.optimizer_actor.zero_grad()
            actor_loss.backward(retain_graph=True)
            torch.nn.utils.clip_grad_norm_(self.actor.parameters(), max_norm=1.0)
            self.optimizer_actor.step()
            
            # Update critic
            self.optimizer_critic.zero_grad()
            critic_loss.backward()
            torch.nn.utils.clip_grad_norm_(self.critic.parameters(), max_norm=1.0)
            self.optimizer_critic.step()
            
            # Append the item loss to the lists
            actor_losses.append(actor_loss.item())  
            critic_losses.append(critic_loss.item())
            
        # Calculate average loss over all epochs
        average_loss_actor = sum(actor_losses) / len(actor_losses)
        average_loss_critic = sum(critic_losses) / len(critic_losses)
        
        return average_loss_actor, average_loss_critic

# Training Function
def train(env, agent, num_episodes, max_timesteps):
    num_targets = 100
    train_targets = CircuitEnvironment.generate_random_targets(num_targets=num_targets, seed=0)
    test_targets = CircuitEnvironment.generate_random_targets(num_targets=20, seed=1)
    
    # Initialize the episode to start from and log lists
    start_episode = 0    
    rewards_log = []
    losses_log = []
    metrics_log = []
    episode_lengths = []
    
    # Attempt to load the saved state
    saved_state = load_training_state()
        
    for episode in range(start_episode, num_episodes):
        if episode >= num_targets:
            break  # Exit the loop if exhausted the number of targets
        
        target = train_targets[episode % len(train_targets)]
        
        node_features_tensor, edge_index, performance_metrics = env.reset(target)
        state = (node_features_tensor, edge_index)
        
        # Initialize storage for episode data
        episode_rewards = []
        states = []
        actions = []
        log_probs = []
        values = []
        masks = []
        perf_metrics_log = []

        for t in range(max_timesteps):            
            action, log_prob, perf_metrics = agent.select_action(state, performance_metrics)
            next_node_features_tensor, next_edge_index, reward, done, previous_metrics = env.step(action, perf_metrics, target)
            
            next_state = (next_node_features_tensor, next_edge_index)
            
            # Append data to the general episode storage
            episode_rewards.append(reward)
            states.append(state)
            actions.append(action)
            log_probs.append(log_prob.item())            
            values.append(agent.critic(node_features_tensor, edge_index).item())
            masks.append(1.0 - float(done))
            perf_metrics_log.append(perf_metrics)
                
            performance_metrics = previous_metrics
            state = next_state            

            # Check for intermediate reward conditions
            if reward <= -200 or reward >= 90:
                # Collecting intermediate step data only
                intermediate_rewards = [reward]
                intermediate_states = [state]
                intermediate_actions = [action]
                intermediate_log_probs = [log_prob.item()]
                intermediate_values = [agent.critic(node_features_tensor, edge_index).item()]
                intermediate_masks = [1.0 - float(done)]

                # Compute returns and advantages for the current intermediate step
                next_value = agent.critic(state[0], state[1]).item() if not done else 0
                returns = agent.compute_gae(next_value, intermediate_rewards, intermediate_masks, intermediate_values)
                advantages = (torch.tensor(returns) - torch.tensor(intermediate_values)).detach().numpy()
                returns = torch.tensor(returns).detach().numpy()
                agent.update_policy(intermediate_states, intermediate_actions, intermediate_log_probs, returns, advantages)

            if done:
                break

        # Final policy update with all accumulated data for the entire episode
        next_value = agent.critic(state[0], state[1]).item() if not done else 0         
        returns = agent.compute_gae(next_value, episode_rewards, masks, values)
    
        advantages = (torch.tensor(returns) - torch.tensor(values)).detach().numpy()
        returns = torch.tensor(returns).detach().numpy()
        average_loss_actor, average_loss_critic = agent.update_policy(states, actions, log_probs, returns, advantages)                        
        save_training_state(agent, episode, rewards_log, losses_log, metrics_log, train_targets, test_targets)

if __name__ == "__main__":
    # Initialize environment parameters
    # Define the values for bounds, and target metrics

    env = CircuitEnvironment(bounds_low, bounds_high)
    agent = PPOAgent(state_dim, action_space, lr_actor, lr_critic, gamma, gae_lambda, epsilon, policy_clip, epochs, entropy_coef)

    num_episodes = 1000
    max_timesteps = 300

    train(env, agent, num_episodes, max_timesteps)