add some codes

codes/dqn_cnn/main.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 10:01:09
+@LastEditor: John
+@LastEditTime: 2020-06-13 00:24:31
+@Discription: 
+@Environment: python 3.7.7
+'''
+'''
+应该是没有收敛，但是pytorch官方教程的结果也差不多
+'''
+import gym
+import torch
+
+from screen_state import get_screen
+from dqn import DQN
+from plot import plot
+
+import argparse
+
+def get_args():
+    '''模型建立好之后只需要在这里调参
+    '''
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--gamma", default=0.999, type=float) # q-learning中的gamma
+    parser.add_argument("--epsilon_start", default=0.9, type=float) # 基于贪心选择action对应的参数epsilon
+    parser.add_argument("--epsilon_end", default=0.05, type=float)
+    parser.add_argument("--epsilon_decay", default=200, type=float)
+
+    parser.add_argument("--memory_capacity", default=10000, type=int,help="capacity of Replay Memory")
+
+    parser.add_argument("--batch_size", default=128, type=int,help="batch size of memory sampling")
+    parser.add_argument("--max_episodes", default=100, type=int)
+    parser.add_argument("--max_steps", default=200, type=int)
+    parser.add_argument("--target_update", default=4, type=int,help="when(every default 10 eisodes) to update target net ")
+    config = parser.parse_args()
+
+    return config
+
+if __name__ == "__main__":
+
+    cfg = get_args()
+    # if gpu is to be used
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    # Get screen size so that we can initialize layers correctly based on shape
+    # returned from AI gym. Typical dimensions at this point are close to 3x40x90
+    # which is the result of a clamped and down-scaled render buffer in get_screen(env,device)
+    env = gym.make('CartPole-v0').unwrapped
+    env.reset()
+    init_screen = get_screen(env, device)
+    _, _, screen_height, screen_width = init_screen.shape
+    # Get number of actions from gym action space
+    n_actions = env.action_space.n
+    agent = DQN(screen_height=screen_height, screen_width=screen_width,
+             n_actions=n_actions, device=device, gamma=cfg.gamma, epsilon_start=cfg.epsilon_start, epsilon_end=cfg.epsilon_end, epsilon_decay=cfg.epsilon_decay, memory_capacity=cfg.memory_capacity,batch_size=cfg.batch_size)
+
+    rewards = []
+    moving_average_rewards = []
+    for i_episode in range(1,cfg.max_episodes+1):
+        # Initialize the environment and state
+        env.reset()
+        last_screen = get_screen(env, device)
+        current_screen = get_screen(env, device)
+        state = current_screen - last_screen
+        ep_reward = 0
+        for t in range(1,cfg.max_steps+1):
+            # Select and perform an action
+            action = agent.select_action(state)     
+            _, reward, done, _ = env.step(action.item())
+            ep_reward += reward
+            reward = torch.tensor([reward], device=device)
+            # Observe new state
+            last_screen = current_screen
+            current_screen = get_screen(env, device)
+
+            if done: break
+            next_state = current_screen - last_screen
+
+            # Store the transition in memory
+            agent.memory.push(state, action, next_state, reward)
+
+            # Move to the next state
+            state = next_state
+
+            # Perform one step of the optimization (on the target network)
+            agent.update()
+
+        # Update the target network, copying all weights and biases in DQN
+        if i_episode % cfg.target_update == 0:
+            agent.target_net.load_state_dict(agent.policy_net.state_dict())
+        print('Episode:', i_episode, ' Reward: %i' %int(ep_reward), 'Explore: %.2f' % agent.epsilon)
+        rewards.append(ep_reward)
+        if i_episode == 1:
+            moving_average_rewards.append(ep_reward)
+        else:
+            moving_average_rewards.append(
+                0.9*moving_average_rewards[-1]+0.1*ep_reward)
+
+    import os
+    import numpy as np
+    output_path = os.path.dirname(__file__)+"/result/"
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+    np.save(output_path+"rewards.npy", rewards)
+    np.save(output_path+"moving_average_rewards.npy", moving_average_rewards)
+    print('Complete！')
+    plot(rewards)
+    plot(moving_average_rewards,ylabel="moving_average_rewards")
+
+