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