#!/usr/bin/env python # coding=utf-8 ''' Author: JiangJi Email: johnjim0816@gmail.com Date: 2022-10-24 08:21:31 LastEditor: JiangJi LastEditTime: 2022-10-26 09:50:49 Discription: Not finished ''' import sys,os os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized." curr_path = os.path.dirname(os.path.abspath(__file__)) # current path parent_path = os.path.dirname(curr_path) # parent path sys.path.append(parent_path) # add path to system path import gym import torch import datetime import numpy as np import argparse from common.utils import all_seed from common.models import MLP from common.memories import ReplayBuffer from common.launcher import Launcher from envs.register import register_env from dqn import DQN import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as T from PIL import Image resize = T.Compose([T.ToPILImage(), T.Resize(40, interpolation=Image.CUBIC), T.ToTensor()]) # xvfb-run -s "-screen 0 640x480x24" python main1.py def get_cart_location(env,screen_width): world_width = env.x_threshold * 2 scale = screen_width / world_width return int(env.state[0] * scale + screen_width / 2.0) # MIDDLE OF CART def get_screen(env): # Returned screen requested by gym is 400x600x3, but is sometimes larger # such as 800x1200x3. Transpose it into torch order (CHW). screen = env.render().transpose((2, 0, 1)) # Cart is in the lower half, so strip off the top and bottom of the screen _, screen_height, screen_width = screen.shape screen = screen[:, int(screen_height*0.4):int(screen_height * 0.8)] view_width = int(screen_width * 0.6) cart_location = get_cart_location(env,screen_width) if cart_location < view_width // 2: slice_range = slice(view_width) elif cart_location > (screen_width - view_width // 2): slice_range = slice(-view_width, None) else: slice_range = slice(cart_location - view_width // 2, cart_location + view_width // 2) # Strip off the edges, so that we have a square image centered on a cart screen = screen[:, :, slice_range] # Convert to float, rescale, convert to torch tensor # (this doesn't require a copy) screen = np.ascontiguousarray(screen, dtype=np.float32) / 255 screen = torch.from_numpy(screen) # Resize, and add a batch dimension (BCHW) return resize(screen) class CNN(nn.Module): def __init__(self, h, w, outputs): super(CNN, self).__init__() self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2) self.bn1 = nn.BatchNorm2d(16) self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2) self.bn2 = nn.BatchNorm2d(32) self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2) self.bn3 = nn.BatchNorm2d(32) # Number of Linear input connections depends on output of conv2d layers # and therefore the input image size, so compute it. def conv2d_size_out(size, kernel_size = 5, stride = 2): return (size - (kernel_size - 1) - 1) // stride + 1 convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w))) convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h))) linear_input_size = convw * convh * 32 self.head = nn.Linear(linear_input_size, outputs) # Called with either one element to determine next action, or a batch # during optimization. Returns tensor([[left0exp,right0exp]...]). def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) return self.head(x.view(x.size(0), -1)) class Main(Launcher): def get_args(self): """ hyperparameters """ curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time parser = argparse.ArgumentParser(description="hyperparameters") parser.add_argument('--algo_name',default='DQN',type=str,help="name of algorithm") parser.add_argument('--env_name',default='CartPole-v1',type=str,help="name of environment") parser.add_argument('--train_eps',default=800,type=int,help="episodes of training") parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") parser.add_argument('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps") parser.add_argument('--gamma',default=0.999,type=float,help="discounted factor") parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon, the higher value, the slower decay") parser.add_argument('--lr',default=0.0001,type=float,help="learning rate") parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity") parser.add_argument('--batch_size',default=128,type=int) parser.add_argument('--target_update',default=4,type=int) parser.add_argument('--hidden_dim',default=256,type=int) parser.add_argument('--device',default='cuda',type=str,help="cpu or cuda") parser.add_argument('--seed',default=10,type=int,help="seed") parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not") parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not") # please manually change the following args in this script if you want parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ '/' + curr_time + '/results' ) parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \ '/' + curr_time + '/models' ) args = parser.parse_args() args = {**vars(args)} # type(dict) return args def env_agent_config(self,cfg): ''' create env and agent ''' env = gym.make('CartPole-v1', new_step_api=True, render_mode='single_rgb_array').unwrapped if cfg['seed'] !=0: # set random seed all_seed(env,seed=cfg["seed"]) try: # state dimension n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n')) except AttributeError: n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape')) n_actions = env.action_space.n # action dimension print(f"n_states: {n_states}, n_actions: {n_actions}") cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters env.reset() init_screen = get_screen(env) _, screen_height, screen_width = init_screen.shape model = CNN(screen_height, screen_width, n_actions) memory = ReplayBuffer(cfg["memory_capacity"]) # replay buffer agent = DQN(model,memory,cfg) # create agent return env, agent def train(self,cfg, env, agent): ''' 训练 ''' print("Start training!") print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}") rewards = [] # record rewards for all episodes steps = [] for i_ep in range(cfg["train_eps"]): ep_reward = 0 # reward per episode ep_step = 0 state = env.reset() # reset and obtain initial state last_screen = get_screen(env) current_screen = get_screen(env) state = current_screen - last_screen for _ in range(cfg['ep_max_steps']): ep_step += 1 action = agent.sample_action(state) # sample action _, reward, done, _,_ = env.step(action) # update env and return transitions last_screen = current_screen current_screen = get_screen(env) next_state = current_screen - last_screen agent.memory.push(state.cpu().numpy(), action, reward, next_state.cpu().numpy(), done) # save transitions state = next_state # update next state for env agent.update() # update agent ep_reward += reward # if done: break if (i_ep + 1) % cfg["target_update"] == 0: # target net update, target_update means "C" in pseucodes agent.target_net.load_state_dict(agent.policy_net.state_dict()) steps.append(ep_step) rewards.append(ep_reward) if (i_ep + 1) % 10 == 0: print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}, step: {ep_step:d}, Epislon: {agent.epsilon:.3f}') print("Finish training!") env.close() res_dic = {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps} return res_dic def test(self,cfg, env, agent): print("Start testing!") print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}") rewards = [] # record rewards for all episodes steps = [] for i_ep in range(cfg['test_eps']): ep_reward = 0 # reward per episode ep_step = 0 state = env.reset() # reset and obtain initial state last_screen = get_screen(env) current_screen = get_screen(env) state = current_screen - last_screen for _ in range(cfg['ep_max_steps']): ep_step+=1 action = agent.predict_action(state) # predict action _, reward, done, _,_ = env.step(action) last_screen = current_screen current_screen = get_screen(env) next_state = current_screen - last_screen state = next_state ep_reward += reward if done: break steps.append(ep_step) rewards.append(ep_reward) print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}") print("Finish testing!") env.close() return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps} if __name__ == "__main__": main = Main() main.run()