add sac discrete

Author: quantumiracle

sac_discrete.py

@@ -0,0 +1,333 @@
+'''
+Soft Actor-Critic version 2
+using target Q instead of V net: 2 Q net, 2 target Q net, 1 policy net
+add alpha loss compared with version 1
+paper: https://arxiv.org/pdf/1812.05905.pdf
+
+Discrete version reference: 
+https://towardsdatascience.com/adapting-soft-actor-critic-for-discrete-action-spaces-a20614d4a50a
+'''
+
+
+import math
+import random
+
+import gym
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.distributions import Categorical
+
+from IPython.display import clear_output
+import matplotlib.pyplot as plt
+from matplotlib import animation
+from IPython.display import display
+
+import argparse
+import time
+
+GPU = True
+device_idx = 0
+if GPU:
+    device = torch.device("cuda:" + str(device_idx) if torch.cuda.is_available() else "cpu")
+else:
+    device = torch.device("cpu")
+print(device)
+
+
+parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
+parser.add_argument('--train', dest='train', action='store_true', default=False)
+parser.add_argument('--test', dest='test', action='store_true', default=False)
+
+args = parser.parse_args()
+
+
+class ReplayBuffer:
+    def __init__(self, capacity):
+        self.capacity = capacity
+        self.buffer = []
+        self.position = 0
+    
+    def push(self, state, action, reward, next_state, done):
+        if len(self.buffer) < self.capacity:
+            self.buffer.append(None)
+        self.buffer[self.position] = (state, action, reward, next_state, done)
+        self.position = int((self.position + 1) % self.capacity)  # as a ring buffer
+    
+    def sample(self, batch_size):
+        batch = random.sample(self.buffer, batch_size)
+        state, action, reward, next_state, done = map(np.stack, zip(*batch)) # stack for each element
+        ''' 
+        the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
+        zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
+        the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
+        np.stack((1,2)) => array([1, 2])
+        '''
+        return state, action, reward, next_state, done
+    
+    def __len__(self):
+        return len(self.buffer)
+        
+class SoftQNetwork(nn.Module):
+    def __init__(self, num_inputs, num_actions, hidden_size, init_w=3e-3):
+        super(SoftQNetwork, self).__init__()
+        
+        self.linear1 = nn.Linear(num_inputs, hidden_size)
+        self.linear2 = nn.Linear(hidden_size, hidden_size)
+        # self.linear3 = nn.Linear(hidden_size, hidden_size)
+        self.linear4 = nn.Linear(hidden_size, num_actions)
+        
+        self.linear4.weight.data.uniform_(-init_w, init_w)
+        self.linear4.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, state):
+        x = F.tanh(self.linear1(state))
+        x = F.tanh(self.linear2(x))
+        # x = F.tanh(self.linear3(x))
+        x = self.linear4(x)
+        return x
+        
+        
+class PolicyNetwork(nn.Module):
+    def __init__(self, num_inputs, num_actions, hidden_size, init_w=3e-3, log_std_min=-20, log_std_max=2):
+        super(PolicyNetwork, self).__init__()
+        
+        self.linear1 = nn.Linear(num_inputs, hidden_size)
+        self.linear2 = nn.Linear(hidden_size, hidden_size)
+        # self.linear3 = nn.Linear(hidden_size, hidden_size)
+        # self.linear4 = nn.Linear(hidden_size, hidden_size)
+
+        self.output = nn.Linear(hidden_size, num_actions)
+
+        self.num_actions = num_actions
+        
+    def forward(self, state, softmax_dim=0):
+        x = F.tanh(self.linear1(state))
+        x = F.tanh(self.linear2(x))
+        # x = F.tanh(self.linear3(x))
+        # x = F.tanh(self.linear4(x))
+
+        probs = F.softmax(self.output(x), dim=softmax_dim)
+        
+        return probs
+    
+    def evaluate(self, state, epsilon=1e-6):
+        '''
+        generate sampled action with state as input wrt the policy network;
+        '''
+        probs = self.forward(state, softmax_dim=-1)
+        log_probs = torch.log(probs)
+        return log_probs
+        
+    def get_action(self, state, deterministic):
+        state = torch.FloatTensor(state).unsqueeze(0).to(device)
+        probs = self.forward(state)
+        dist = Categorical(probs)
+
+        if deterministic:
+            action = np.argmax(probs.detach().cpu().numpy())
+        else:
+            action = dist.sample().squeeze().detach().cpu().numpy()
+        return action
+
+
+class SAC_Trainer():
+    def __init__(self, replay_buffer, hidden_dim):
+        self.replay_buffer = replay_buffer
+
+        self.soft_q_net1 = SoftQNetwork(state_dim, action_dim, hidden_dim).to(device)
+        self.soft_q_net2 = SoftQNetwork(state_dim, action_dim, hidden_dim).to(device)
+        self.target_soft_q_net1 = SoftQNetwork(state_dim, action_dim, hidden_dim).to(device)
+        self.target_soft_q_net2 = SoftQNetwork(state_dim, action_dim, hidden_dim).to(device)
+        self.policy_net = PolicyNetwork(state_dim, action_dim, hidden_dim).to(device)
+        self.log_alpha = torch.zeros(1, dtype=torch.float32, requires_grad=True, device=device)
+        print('Soft Q Network (1,2): ', self.soft_q_net1)
+        print('Policy Network: ', self.policy_net)
+
+        for target_param, param in zip(self.target_soft_q_net1.parameters(), self.soft_q_net1.parameters()):
+            target_param.data.copy_(param.data)
+        for target_param, param in zip(self.target_soft_q_net2.parameters(), self.soft_q_net2.parameters()):
+            target_param.data.copy_(param.data)
+
+        self.soft_q_criterion1 = nn.MSELoss()
+        self.soft_q_criterion2 = nn.MSELoss()
+
+        soft_q_lr = 3e-4
+        policy_lr = 3e-4
+        alpha_lr  = 3e-4
+
+        self.soft_q_optimizer1 = optim.Adam(self.soft_q_net1.parameters(), lr=soft_q_lr)
+        self.soft_q_optimizer2 = optim.Adam(self.soft_q_net2.parameters(), lr=soft_q_lr)
+        self.policy_optimizer = optim.Adam(self.policy_net.parameters(), lr=policy_lr)
+        self.alpha_optimizer = optim.Adam([self.log_alpha], lr=alpha_lr)
+
+    
+    def update(self, batch_size, reward_scale=10., auto_entropy=True, target_entropy=-2, gamma=0.99, soft_tau=1e-2):
+        state, action, reward, next_state, done = self.replay_buffer.sample(batch_size)
+        # print('sample:', state, action,  reward, done)
+
+        state      = torch.FloatTensor(state).to(device)
+        next_state = torch.FloatTensor(next_state).to(device)
+        action     = torch.Tensor(action).to(torch.int64).to(device)
+        reward     = torch.FloatTensor(reward).unsqueeze(1).to(device)  # reward is single value, unsqueeze() to add one dim to be [reward] at the sample dim;
+        done       = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(device)
+        predicted_q_value1 = self.soft_q_net1(state)
+        predicted_q_value1 = predicted_q_value1.gather(1, action.unsqueeze(-1))
+        predicted_q_value2 = self.soft_q_net2(state)
+        predicted_q_value2 = predicted_q_value2.gather(1, action.unsqueeze(-1))
+        log_prob = self.policy_net.evaluate(state)
+        next_log_prob = self.policy_net.evaluate(next_state)
+        # reward = reward_scale * (reward - reward.mean(dim=0)) / (reward.std(dim=0) + 1e-6) # normalize with batch mean and std; plus a small number to prevent numerical problem
+
+    # Training Q Function
+        # print((next_log_prob.exp()*self.target_soft_q_net2(next_state)).shape,  next_log_prob.shape)
+        target_q_min = (next_log_prob.exp() * (torch.min(self.target_soft_q_net1(next_state),self.target_soft_q_net2(next_state)) - self.alpha * next_log_prob)).sum(dim=-1).unsqueeze(-1)
+        target_q_value = reward + (1 - done) * gamma * target_q_min # if done==1, only reward
+        q_value_loss1 = self.soft_q_criterion1(predicted_q_value1, target_q_value.detach())  # detach: no gradients for the variable
+        q_value_loss2 = self.soft_q_criterion2(predicted_q_value2, target_q_value.detach())
+
+        self.soft_q_optimizer1.zero_grad()
+        q_value_loss1.backward()
+        self.soft_q_optimizer1.step()
+        self.soft_q_optimizer2.zero_grad()
+        q_value_loss2.backward()
+        self.soft_q_optimizer2.step()  
+
+    # Training Policy Function
+        predicted_new_q_value = torch.min(self.soft_q_net1(state),self.soft_q_net2(state))
+        policy_loss = (log_prob.exp()*(self.alpha * log_prob - predicted_new_q_value)).sum(dim=-1).mean()
+
+        self.policy_optimizer.zero_grad()
+        policy_loss.backward()
+        self.policy_optimizer.step()
+        
+        # print('q loss: ', q_value_loss1, q_value_loss2)
+        # print('policy loss: ', policy_loss )
+
+    # Soft update the target value net
+        for target_param, param in zip(self.target_soft_q_net1.parameters(), self.soft_q_net1.parameters()):
+            target_param.data.copy_(  # copy data value into target parameters
+                target_param.data * (1.0 - soft_tau) + param.data * soft_tau
+            )
+        for target_param, param in zip(self.target_soft_q_net2.parameters(), self.soft_q_net2.parameters()):
+            target_param.data.copy_(  # copy data value into target parameters
+                target_param.data * (1.0 - soft_tau) + param.data * soft_tau
+            )
+
+        # Updating alpha wrt entropy
+        # alpha = 0.0  # trade-off between exploration (max entropy) and exploitation (max Q) 
+        if auto_entropy is True:
+            alpha_loss = -(self.log_alpha * (log_prob + target_entropy).detach()).mean()
+            # print('alpha loss: ',alpha_loss)
+            self.alpha_optimizer.zero_grad()
+            alpha_loss.backward()
+            self.alpha_optimizer.step()
+            self.alpha = self.log_alpha.exp()
+        else:
+            self.alpha = 1.
+            alpha_loss = 0
+            
+        return predicted_new_q_value.mean()
+
+
+    def save_model(self, path):
+        torch.save(self.soft_q_net1.state_dict(), path+'_q1')
+        torch.save(self.soft_q_net2.state_dict(), path+'_q2')
+        torch.save(self.policy_net.state_dict(), path+'_policy')
+
+    def load_model(self, path):
+        self.soft_q_net1.load_state_dict(torch.load(path+'_q1'))
+        self.soft_q_net2.load_state_dict(torch.load(path+'_q2'))
+        self.policy_net.load_state_dict(torch.load(path+'_policy'))
+
+        self.soft_q_net1.eval()
+        self.soft_q_net2.eval()
+        self.policy_net.eval()
+
+
+def plot(rewards):
+    clear_output(True)
+    plt.figure(figsize=(20,5))
+    plt.plot(rewards)
+    plt.savefig('sac_v2.png')
+    # plt.show()
+
+
+replay_buffer_size = 1e6
+replay_buffer = ReplayBuffer(replay_buffer_size)
+
+# choose env
+env = gym.make('CartPole-v1')
+state_dim  = env.observation_space.shape[0]
+action_dim = env.action_space.n  # discrete
+
+# hyper-parameters for RL training
+max_episodes  = 10000
+max_steps = 100
+frame_idx   = 0
+batch_size  = 256
+update_itr = 1
+AUTO_ENTROPY=False
+DETERMINISTIC=False
+hidden_dim = 64
+rewards     = []
+model_path = './model/sac_discrete_v2'
+# target_entropy = -1.*action_dim
+target_entropy = 0.98 * -np.log(1 / action_dim)
+
+sac_trainer=SAC_Trainer(replay_buffer, hidden_dim=hidden_dim)
+
+if __name__ == '__main__':
+    if args.train:
+        # training loop
+        for eps in range(max_episodes):
+            state =  env.reset()
+            episode_reward = 0
+            
+            
+            for step in range(max_steps):
+                action = sac_trainer.policy_net.get_action(state, deterministic = DETERMINISTIC)
+                next_state, reward, done, _ = env.step(action)
+                # env.render()       
+                    
+                replay_buffer.push(state, action, reward, next_state, done)
+                
+                state = next_state
+                episode_reward += reward
+                frame_idx += 1
+                
+                
+                if len(replay_buffer) > batch_size:
+                    for i in range(update_itr):
+                        _=sac_trainer.update(batch_size, reward_scale=1., auto_entropy=AUTO_ENTROPY, target_entropy=target_entropy)
+
+                if done:
+                    break
+
+            if eps % 20 == 0 and eps>0: # plot and model saving interval
+                plot(rewards)
+                np.save('rewards', rewards)
+                sac_trainer.save_model(model_path)
+            print('Episode: ', eps, '| Episode Reward: ', episode_reward, '| Episode Length: ', step)
+            rewards.append(episode_reward)
+        sac_trainer.save_model(model_path)
+
+    if args.test:
+        sac_trainer.load_model(model_path)
+        for eps in range(10):
+            state =  env.reset()
+            episode_reward = 0
+
+            for step in range(max_steps):
+                action = sac_trainer.policy_net.get_action(state, deterministic = DETERMINISTIC)
+                next_state, reward, done, _ = env.step(action)
+                env.render()   
+
+
+                episode_reward += reward
+                state=next_state
+
+            print('Episode: ', eps, '| Episode Reward: ', episode_reward)