human
2.2 Reward shaping

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#Here is the Python code you requested.
class BaseIntrinsicRewardModule(nn.Module):
    def __init__(self):
        super().__init__()

    def get_intrinsic_reward(self, state, action, next_state):
        return 0.0

    def get_loss(self, state_batch, action_batch, next_state_batch):
        pass

def train_with_reward(env, agent, reward_module, n_episodes=100, update_reward_period=100, batch_size=100, n_iter=10):
    buffer = ReplayBuffer(size=int(1e6))
    
    if list(reward_module.parameters()):
        optimizer = torch.optim.Adam(reward_module.parameters())
    else:
        optimizer = None

    losses = []
    s = env.reset()

    for i in range(n_episodes):
        done = False
        
        while not done:
            i_s = get_state_number(s)
            a = agent.get_action(i_s)
            s_next, r, done, _  = env.step(a)
            i_s_next = get_state_number(s_next)
            
            state_t = torch.tensor(s).float().view(1, -1)
            action_t = torch.tensor(a).float().view(1, -1)
            next_state_t = torch.tensor(s_next).float().view(1, -1)

            r_intr = reward_module.get_intrinsic_reward(state_t, action_t, next_state_t)
            r += r_intr

            agent.update(i_s, a, r, i_s_next, done)
            buffer.add(s, a, r, s_next, done)

            s = s_next

        if (i + 1) % update_reward_period == 0 and optimizer is not None:
        
            for _ in range(n_iter):
                optimizer.zero_grad()
                state_batch, action_batch, _, next_state_batch, _ = buffer.sample(batch_size)
                
                state_tensor = torch.tensor(state_batch).float().flatten(1, 2)
                action_tensor = torch.tensor(action_batch).float().view(-1, 1)
                next_state_tensor = torch.tensor(next_state_batch).float().flatten(1, 2)
                
                loss = reward_module.get_loss(state_tensor, action_tensor, next_state_tensor)
                loss.backward()
                optimizer.step()
                losses.append(loss.item())
           ...