hot update

projects/codes/SoftQ/softq.py

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