How to in enhance A3C entropy?

Rabee_Qasem: <p>I’m trying to implement this A3C <a href="https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/PolicyGradient/A3C/pytorch/a3c.py" rel="noopener nofollow ugc">code</a> in my custom environment, and I have a basic understanding of the algorithm. The algorithm worked, but it did not give me a good performance. I looked into multiple implementations, and each one seemed different to me, like this <a href="https://github.com/wuhuikai/a3c-deepmind-pytorch" rel="noopener nofollow ugc">one</a>, for example, now the algorithm that I write is as follows:</p>
<ul>
<li>A3C</li>
</ul>
<pre><code class="lang-auto">class ActorCritics(nn.Module):
  def __init__(self,input,n_actions,env,gamma=0.99):
    super(ActorCritics,self).__init__()
    self.gamma=gamma
    self.env=env
    self.n_actions=n_actions

    self.pi1=nn.Linear(input,128)
    self.v1=nn.Linear(input,128)

    self.pi2=nn.Linear(128,64)
    self.v2=nn.Linear(128,64)

    self.pi3=nn.Linear(64,32)
    self.v3=nn.Linear(64,32)

    self.pi4=nn.Linear(32,16)
    self.v4=nn.Linear(32,16)

    self.pi5=nn.Linear(16,8)
    self.v5=nn.Linear(16,8)

    self.pi6=nn.Linear(8,4)
    self.v6=nn.Linear(8,4)

    self.pi7=nn.Linear(4,2)
    self.v7=nn.Linear(4,2)

    self.pi=nn.Linear(2,n_actions)
    self.v=nn.Linear(2,1)

    self.rewards=[]
    self.actions=[]
    self.states=[]
  
  #this function takes the values of the state,actions,and reward and append to the memory
  def remember(self,state,action,reward):
    self.actions.append(action)
    self.rewards.append(reward)
    self.states.append(state)
  
  #this function reset the memory each time we are calling the learning function
  def clear_memory(self):
    self.states=[]
    self.actions=[]
    self.rewards=[]

  def forward(self,state):
    pi1=F.relu(self.pi1(state))
    v1=F.relu(self.v1(state))

    pi2=F.relu(self.pi2(pi1))
    v2=F.relu(self.v2(v1))

    pi3=F.relu(self.pi3(pi2))
    v3=F.relu(self.v3(v2))

    pi4=F.relu(self.pi4(pi3))
    v4=F.relu(self.v4(v3))

    pi5=F.relu(self.pi5(pi4))
    v5=F.relu(self.v5(v4))

    pi6=F.relu(self.pi6(pi5))
    v6=F.relu(self.v6(v5))

    pi7=F.relu(self.pi7(pi6))
    v7=F.relu(self.v7(v6))

    pi=self.pi(pi7)
    v=self.v(v7)
    return pi,v
  
  def calc_returns(self,done,vstates):
    p,v=self.forward(vstates)

    R=v[-1]*(1-int(done))
    batch_return=[]

    for reward in self.rewards[::-1]:
      R=reward+self.gamma*R
      batch_return.append(R)
    batch_return.reverse()
    batch_return=T.tensor(batch_return,dtype=float)
    return batch_return


  def calc_loss(self,done):
    #states=T.tensor(self.states,dtype=T.float)
    list_state=[]
    if len(self.states)&gt;1:
      for lstate in self.states:
        soruce,end=self.env.state_dec(lstate)
        state_v=self.env.state_to_vector(soruce,end)
        list_state.append(state_v)
      states=T.tensor(list_state)

    else:
      soruce,end=self.env.state_dec(self.states[0])
      state_v=self.env.state_to_vector(soruce,end)
      list_state.append(state_v)
      states=T.tensor([list_state])
    actions=T.tensor(self.actions,dtype=T.float)


    returns=self.calc_returns(done,states)


    p,values=self.forward(states)
    values=values.squeeze()

    critic_loss=(returns-values)**2

    probs=T.softmax(p,dim=1)
    dist=Categorical(probs)
    log_probs=dist.log_prob(actions)
    actor_loss=-log_probs*(returns-values)
    total_loss=(critic_loss+actor_loss).mean()

    return total_loss

  def choose_action(self,node,action):
      state_vector=self.env.state_to_vector(node,action)
      state=T.tensor([state_vector],dtype=T.float)
      pi,v=self.forward(state)
      probs=T.softmax(pi,dim=1)
      dist=Categorical(probs)
      action=dist.sample().numpy()[0]#take a sample from the categorical dist from 1-22
      return action

</code></pre>
<ul>
<li>AGENT</li>
</ul>
<pre><code class="lang-auto">class Agent(mp.Process):
 def __init__(self,global_actor_critic,optimizer,input,n_actions
              ,gamma,lr,worker_name,global_episode_index,env,gather,games,T_max,res_queue,loss_queue):
   super(Agent,self).__init__()
   self.local_actor_critic=ActorCritics(input,n_actions,env,gamma)
   self.global_actor_critic=global_actor_critic
   self.worker_name='w%02i'%worker_name
   self.episode_idx = global_episode_index
   self.env=env
   self.gather_eps=gather
   self.optimizer=optimizer
   self.N_games=games
   self.T_max=T_max
   self.res_queue=res_queue
   self.loss_queue=loss_queue
   self.dict_list={'number_of_episodes':[],'score':[],'loss':[]}

 def list_remember(self,d_episode,d_score,d_loss):
  self.dict_list['number_of_episodes'].append(d_episode)#,d_score,d_loss.item()
  self.dict_list['score'].append(d_score)
  self.dict_list['loss'].append(d_loss.item())
 
 def run(self):
    t_step=1
    max_itr=1000
    #self.episode_idx is a gloabl parametar from MP class and we need to get the value from it
    while self.episode_idx.value &lt; self.N_games:



      itr=0
      done=False
      observation=self.env.reset()
      score=0
      penalties=0

      self.local_actor_critic.clear_memory()
      while not done:
        soruce,end=self.env.state_dec(observation)
        action=self.local_actor_critic.choose_action(soruce,end)
        observation_,reward,done=self.env.step(observation,action)
        if reward == -1000:
          penalties+=1
        
        score += reward
        self.local_actor_critic.remember(observation,action,reward)

        if t_step% self.T_max==0 or done:
          loss=self.local_actor_critic.calc_loss(done)
          self.optimizer.zero_grad()
          loss.backward()
          #set the current parameters for the workers into the gloabl parameters
          for local_param,global_param in zip(self.local_actor_critic.parameters(),
                                               self.global_actor_critic.parameters()):
            global_param._grad=local_param.grad
          self.optimizer.step()
          self.local_actor_critic.load_state_dict(self.global_actor_critic.state_dict())
          self.local_actor_critic.clear_memory()

        t_step+=1

        itr+=1
        observation=observation_
      print(self.worker_name,'episode',self.episode_idx.value,'reward',score,'penalties',penalties,'goal',done,
        'itr_to_done',itr,'loss',loss.item(),'\n',flush=True)

      self.list_remember(self.episode_idx.value,score,loss)

      self.gather_eps.append_data(self.episode_idx.value,score,loss.item())
      self.res_queue.put(score)
      self.loss_queue.put(loss.item())


      with self.episode_idx.get_lock():
        self.episode_idx.value+=1


    self.res_queue.put(None)
    self.loss_queue.put(None)


</code></pre>
<p>now my question did I implement the algorithm right and is the entropy of my code is also written right, and how can I enhance the <a href="https://colab.research.google.com/drive/15bq8xAEwPaqUAqHkfoJw8LSETQuXTxm8?usp=sharing" rel="noopener nofollow ugc">code</a>?</p>