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car_racing.py
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car_racing.py
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import torch
import gym
import numpy as np
from TD3 import TD3
from DDPG import DDPG
from utils import NaivePrioritizedBuffer
import os
import roboschool, gym
from PIL import Image
import matplotlib.pyplot as plt
import torch.optim as optim
import Box2D
import pdb
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from utils import DrawLine
import math
import argparse
from torch.distributions import Beta
from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
parser = argparse.ArgumentParser(description='Train a TD3 agent for the CarRacing-v0')
parser.add_argument('policy', help='Choose policy')
args = parser.parse_args()
class Env():
"""
Environment wrapper for CarRacing
"""
def __init__(self, env_name, random_seed, img_stack, action_repeat):
self.env = gym.make(env_name)
self.env.seed(random_seed)
self.action_space = self.env.action_space
self.reward_threshold = self.env.spec.reward_threshold
self.img_stack = img_stack
self.action_repeat = action_repeat
def reset(self):
self.counter = 0
self.av_r = self.reward_memory()
self.die = False
img_rgb = self.env.reset()
# print(img_rgb)
img_gray = self.rgb2gray(img_rgb)
self.stack = [np.expand_dims(img_gray, axis=0)] * self.img_stack # four frames for decision
return torch.FloatTensor(self.stack).permute(1, 0, 2, 3)
def step(self, action):
total_reward = 0
for i in range(self.action_repeat):
img_rgb, reward, die, _ = self.env.step(action)
# don't penalize "die state"
if die:
reward += 100
# green penalty
if np.mean(img_rgb[:, :, 1]) > 185.0:
reward -= 0.05
total_reward += reward
# if no reward recently, end the episode
done = True if self.av_r(reward) <= -0.1 else False
if done or die:
break
img_gray = self.rgb2gray(img_rgb)
self.stack.pop(0)
self.stack.append(np.expand_dims(img_gray, axis=0))
assert len(self.stack) == self.img_stack
return torch.FloatTensor(self.stack).permute(1, 0, 2, 3), total_reward, done, die
def render(self, *arg):
self.env.render(*arg)
@staticmethod
def rgb2gray(rgb, norm=True):
# rgb image -> gray [0, 1]
gray = np.dot(rgb[..., :], [0.299, 0.587, 0.114])
if norm:
# normalize
gray = gray / 128. - 1.
return gray
@staticmethod
def reward_memory():
# record reward for last 100 steps
count = 0
length = 100
history = np.zeros(length)
def memory(reward):
nonlocal count
history[count] = reward
count = (count + 1) % length
return np.mean(history)
return memory
def train(env):
######### Hyperparameters #########
env_name = env
log_interval = 10 # print avg reward after interval
random_seed = 0
gamma = 0.99 # discount for future rewards
batch_size = 100 # num of transitions sampled from replay buffer
lr = 0.001
exploration_noise = 0.5
polyak = 0.995 # target policy update parameter (1-tau)
policy_noise = 0.2 # target policy smoothing noise
noise_clip = 0.5
policy_delay = 2 # delayed policy updates parameter
max_episodes = int(1e8) # max num of episodes
max_timesteps = 500 # max timesteps in one episode
save_every = 100 # model saving interal
img_stack = 4 # number of image stacks together
action_repeat = 8 # repeat action in N frames
max_size = 1e6
vis = True
""" parameters for epsilon declay """
epsilon_start = 1
epsilon_final = 0.01
decay_rate = max_episodes / 50
""" beta Prioritized Experience Replay"""
beta_start = 0.4
beta_frames = 25000
# if not os.path.exists('./TD3tested'):
# os.mkdir('./TD3tested')
directory = "./{}".format(env_name) # save trained models
filename = "TD3_{}_{}".format(env_name, random_seed)
###################################
env = Env(env_name, random_seed, img_stack, action_repeat)
# print("env")
action_dim = env.action_space.shape[0]
# if vis:
# draw_reward = DrawLine(env="car", title="PPO", xlabel="Episode", ylabel="Moving averaged episode reward")
if args.policy == 'TD3':
policy = TD3(action_dim, img_stack)
if args.policy == 'DDPG':
policy = DDPG(action_dim, img_stack)
replay_buffer = NaivePrioritizedBuffer(int(max_size))
if random_seed:
print("Random Seed: {}".format(random_seed))
torch.manual_seed(random_seed)
# logging variables:
log_f = open("log.txt", "w+")
## for plot
Reward = []
total_timesteps = 0
episode_timesteps = 0
running_score = 0
# training procedure:
for episode in range(1, max_episodes + 1):
state = env.reset()
# print("here")
episode_timesteps = 0
score = 0
for t in range(max_timesteps):
# select action and add exploration noise:
# print("state: " + str(state))
action = policy.select_action(state)
# print("action: " + str(action))
exploration_noise = (epsilon_start - epsilon_final) * math.exp(-1. * total_timesteps / decay_rate)
action = action + np.random.normal(0, exploration_noise, size=action_dim)
action = action.clip(env.action_space.low, env.action_space.high)
# print("action clipped: " + str(action))
# take action in env:
next_state, reward, done, die = env.step( action * np.array([2., 1., 1.]) + np.array([-1., 0., 0.]) )
# print("state: " +str(next_state))
env.render()
replay_buffer.add(state, next_state, action, reward, float(done))
state = next_state
score += reward
total_timesteps += 1
episode_timesteps += 1
# if episode is done then update policy:
if done or t == (max_timesteps - 1):
beta = min(1.0, beta_start + total_timesteps * (1.0 - beta_start) / beta_frames)
policy.train(replay_buffer, episode_timesteps, beta)
break
running_score = running_score * 0.99 + score * 0.01
if episode % log_interval == 0:
# if vis:
# draw_reward(xdata = episode, ydata = running_score)
log_f.write('Ep {}\tLast score: {:.2f}\tMoving average score: {:.2f}\n'.format(episode, score, running_score))
log_f.flush()
print('Ep {}\tLast score: {:.2f}\tMoving average score: {:.2f}'.format(episode, score, running_score))
# if avg reward > 300 then save and stop traning:
if running_score >= 900:
# if episode % save_every == 0:
print("########## Model received ###########")
name = filename
policy.save(directory, name)
log_f.close()
break
if episode % 100 == 0:
if not os.path.exists(directory):
os.mkdir(directory)
policy.save(directory, filename)
if __name__ == "__main__":
train('CarRacing-v0')