71 lines
3.6 KiB
Python
71 lines
3.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from collections import deque
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import random
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from torch.distributions import Categorical
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import gym
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import numpy as np
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class SoftQ:
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def __init__(self,n_actions,model,memory,cfg):
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self.memory = memory
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self.alpha = cfg.alpha
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self.gamma = cfg.gamma # discount factor
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self.batch_size = cfg.batch_size
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self.device = torch.device(cfg.device)
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self.policy_net = model.to(self.device)
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self.target_net = model.to(self.device)
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self.target_net.load_state_dict(self.policy_net.state_dict()) # copy parameters
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self.optimizer = torch.optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
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self.losses = [] # save losses
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def sample_action(self,state):
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state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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with torch.no_grad():
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q = self.policy_net(state)
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v = self.alpha * torch.log(torch.sum(torch.exp(q/self.alpha), dim=1, keepdim=True)).squeeze()
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dist = torch.exp((q-v)/self.alpha)
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dist = dist / torch.sum(dist)
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c = Categorical(dist)
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a = c.sample()
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return a.item()
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def predict_action(self,state):
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state = torch.tensor(np.array(state), device=self.device, dtype=torch.float).unsqueeze(0)
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with torch.no_grad():
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q = self.policy_net(state)
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v = self.alpha * torch.log(torch.sum(torch.exp(q/self.alpha), dim=1, keepdim=True)).squeeze()
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dist = torch.exp((q-v)/self.alpha)
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dist = dist / torch.sum(dist)
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c = Categorical(dist)
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a = c.sample()
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return a.item()
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def update(self):
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if len(self.memory) < self.batch_size: # when the memory capacity does not meet a batch, the network will not update
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return
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state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
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state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
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action_batch = torch.tensor(np.array(action_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
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reward_batch = torch.tensor(np.array(reward_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
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next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float) # shape(batchsize,n_states)
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done_batch = torch.tensor(np.array(done_batch), device=self.device, dtype=torch.float).unsqueeze(1) # shape(batchsize,1)
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# print(state_batch.shape,action_batch.shape,reward_batch.shape,next_state_batch.shape,done_batch.shape)
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with torch.no_grad():
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next_q = self.target_net(next_state_batch)
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next_v = self.alpha * torch.log(torch.sum(torch.exp(next_q/self.alpha), dim=1, keepdim=True))
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y = reward_batch + (1 - done_batch ) * self.gamma * next_v
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loss = F.mse_loss(self.policy_net(state_batch).gather(1, action_batch.long()), y)
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self.losses.append(loss)
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self.optimizer.zero_grad()
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loss.backward()
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self.optimizer.step()
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def save_model(self, path):
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from pathlib import Path
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# create path
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Path(path).mkdir(parents=True, exist_ok=True)
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torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
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def load_model(self, path):
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self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
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for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
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param.data.copy_(target_param.data) |