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johnjim0816
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projects/codes/PPO/ppo2.py
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162
projects/codes/PPO/ppo2.py
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#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2021-03-23 15:17:42
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LastEditor: John
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LastEditTime: 2021-12-31 19:38:33
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Discription:
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Environment:
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'''
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import os
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import numpy as np
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import torch
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import torch.optim as optim
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import torch.nn as nn
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from torch.distributions.categorical import Categorical
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class PPOMemory:
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def __init__(self, batch_size):
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self.states = []
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self.probs = []
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self.vals = []
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self.actions = []
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self.rewards = []
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self.dones = []
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self.batch_size = batch_size
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def sample(self):
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batch_step = np.arange(0, len(self.states), self.batch_size)
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indices = np.arange(len(self.states), dtype=np.int64)
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np.random.shuffle(indices)
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batches = [indices[i:i+self.batch_size] for i in batch_step]
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return np.array(self.states),np.array(self.actions),np.array(self.probs),\
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np.array(self.vals),np.array(self.rewards),np.array(self.dones),batches
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def push(self, state, action, probs, vals, reward, done):
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self.states.append(state)
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self.actions.append(action)
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self.probs.append(probs)
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self.vals.append(vals)
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self.rewards.append(reward)
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self.dones.append(done)
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def clear(self):
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self.states = []
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self.probs = []
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self.actions = []
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self.rewards = []
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self.dones = []
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self.vals = []
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class Actor(nn.Module):
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def __init__(self,n_states, n_actions,
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hidden_dim):
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super(Actor, self).__init__()
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self.actor = nn.Sequential(
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nn.Linear(n_states, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, n_actions),
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nn.Softmax(dim=-1)
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)
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def forward(self, state):
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dist = self.actor(state)
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dist = Categorical(dist)
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return dist
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class Critic(nn.Module):
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def __init__(self, n_states,hidden_dim):
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super(Critic, self).__init__()
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self.critic = nn.Sequential(
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nn.Linear(n_states, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, 1)
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)
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def forward(self, state):
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value = self.critic(state)
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return value
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class PPO:
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def __init__(self, n_states, n_actions,cfg):
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self.gamma = cfg.gamma
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self.continuous = cfg.continuous
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self.policy_clip = cfg.policy_clip
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self.n_epochs = cfg.n_epochs
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self.gae_lambda = cfg.gae_lambda
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self.device = cfg.device
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self.actor = Actor(n_states, n_actions,cfg.hidden_dim).to(self.device)
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self.critic = Critic(n_states,cfg.hidden_dim).to(self.device)
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self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr)
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self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=cfg.critic_lr)
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self.memory = PPOMemory(cfg.batch_size)
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self.loss = 0
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def choose_action(self, state):
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state = np.array([state]) # 先转成数组再转tensor更高效
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state = torch.tensor(state, dtype=torch.float).to(self.device)
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dist = self.actor(state)
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value = self.critic(state)
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action = dist.sample()
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probs = torch.squeeze(dist.log_prob(action)).item()
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if self.continuous:
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action = torch.tanh(action)
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else:
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action = torch.squeeze(action).item()
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value = torch.squeeze(value).item()
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return action, probs, value
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def update(self):
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for _ in range(self.n_epochs):
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state_arr, action_arr, old_prob_arr, vals_arr,reward_arr, dones_arr, batches = self.memory.sample()
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values = vals_arr[:]
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### compute advantage ###
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advantage = np.zeros(len(reward_arr), dtype=np.float32)
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for t in range(len(reward_arr)-1):
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discount = 1
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a_t = 0
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for k in range(t, len(reward_arr)-1):
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a_t += discount*(reward_arr[k] + self.gamma*values[k+1]*\
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(1-int(dones_arr[k])) - values[k])
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discount *= self.gamma*self.gae_lambda
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advantage[t] = a_t
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advantage = torch.tensor(advantage).to(self.device)
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### SGD ###
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values = torch.tensor(values).to(self.device)
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for batch in batches:
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states = torch.tensor(state_arr[batch], dtype=torch.float).to(self.device)
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old_probs = torch.tensor(old_prob_arr[batch]).to(self.device)
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actions = torch.tensor(action_arr[batch]).to(self.device)
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dist = self.actor(states)
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critic_value = self.critic(states)
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critic_value = torch.squeeze(critic_value)
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new_probs = dist.log_prob(actions)
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prob_ratio = new_probs.exp() / old_probs.exp()
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weighted_probs = advantage[batch] * prob_ratio
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weighted_clipped_probs = torch.clamp(prob_ratio, 1-self.policy_clip,
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1+self.policy_clip)*advantage[batch]
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actor_loss = -torch.min(weighted_probs, weighted_clipped_probs).mean()
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returns = advantage[batch] + values[batch]
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critic_loss = (returns-critic_value)**2
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critic_loss = critic_loss.mean()
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total_loss = actor_loss + 0.5*critic_loss
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self.loss = total_loss
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self.actor_optimizer.zero_grad()
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self.critic_optimizer.zero_grad()
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total_loss.backward()
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self.actor_optimizer.step()
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self.critic_optimizer.step()
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self.memory.clear()
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def save(self,path):
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actor_checkpoint = os.path.join(path, 'ppo_actor.pt')
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critic_checkpoint= os.path.join(path, 'ppo_critic.pt')
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torch.save(self.actor.state_dict(), actor_checkpoint)
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torch.save(self.critic.state_dict(), critic_checkpoint)
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def load(self,path):
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actor_checkpoint = os.path.join(path, 'ppo_actor.pt')
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critic_checkpoint= os.path.join(path, 'ppo_critic.pt')
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self.actor.load_state_dict(torch.load(actor_checkpoint))
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self.critic.load_state_dict(torch.load(critic_checkpoint))
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