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play_mario.py
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play_mario.py
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import numpy as np
import random
import math
import cv2
import os
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
from net_pytorch import dqn_net
from replay_memory import replay_memory
from data import env
import matplotlib
# if gpu is to be used
use_cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if use_cuda else torch.ByteTensor
Tensor = FloatTensor
def ob_process(frame):
'''
Parameters
----------
frame: {ndarray} of shape (90,90)
Returns
-------
frame: {Tensor} of shape torch.Size([1,84,84])
'''
frame = cv2.resize(frame, (84, 84), interpolation=cv2.INTER_AREA)
#frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame=frame.astype('float64')
frame=torch.from_numpy(frame)
frame=frame.unsqueeze(0).type(Tensor)
return frame
def plot_graph(mean_reward_list):
plt.figure(1)
plt.clf()
plt.title('Episode Total Reward')
plt.xlabel('Episode')
plt.ylabel('Total Reward')
# 最近100个episode的total reward的平均值 #
plt.plot(mean_reward_list)
plt.pause(0.001) # pause a bit so that plots are updated
if is_ipython:
display.clear_output(wait=True)
display.display(plt.gcf())
def learn(env,
MAX_EPISODE,
EPS_START,
EPS_END,
EPS_DECAY,
ACTION_NUM,
REPLAY_MEMORY_CAPACITY,
BATCH_SIZE,
LOSS_FUNCTION,
OPTIM_METHOD,
LEARNING_RATE,
GAMMA,
NET_COPY_STEP,
OBSERVE,
TRAIN_FREQ,
PATH
):
### initialization ###
action_space=[(0,Tensor([1,0,0,0,0,0])),
(3,Tensor([0,1,0,0,0,0])),
(7,Tensor([0,0,1,0,0,0])),
(11,Tensor([0,0,0,1,0,0])),
(4,Tensor([0,0,0,0,1,0])),
(8,Tensor([0,0,0,0,0,1]))]
# (action_button , action_onehot)
# 以上动作分别为：不动、左走、右走、跳、左跳、右跳
value_net = dqn_net(ACTION_NUM)
target_net=dqn_net(ACTION_NUM)
if torch.cuda.is_available():
value_net.cuda()
target_net.cuda()
if os.path.isfile(PATH):
value_net.load_state_dict(torch.load(PATH))
buffer=replay_memory(REPLAY_MEMORY_CAPACITY)
env.reset()
obs,_,_,_,_,_,_=env.step(0)
obs=ob_process(obs)
obs4=torch.cat(([obs,obs,obs,obs]),dim=0) # {Tensor} of shape torch.Size([4,84,84])
judge_distance=0
episode_total_reward = 0
epi_total_reward_list=[]
mean_reward_list=[]
# counters #
time_step=0
update_times=0
episode_num=0
history_distance=200
while episode_num <= MAX_EPISODE:
### choose an action with epsilon-greedy ###
prob = random.random()
threshold = EPS_END + (EPS_START - EPS_END) * math.exp(-1 * episode_num / EPS_DECAY)
if prob <= threshold:
action_index = np.random.randint(6)
action_button = action_space[action_index][0] # {int}
action_onehot = action_space[action_index][1] # {Tensor}
else:
action_button, action_onehot = value_net.select_action(obs4)
### do one step ###
obs_next, reward, done, _, max_distance, _, now_distance = env.step(action_button)
obs_next = ob_process(obs_next)
obs4_next = torch.cat(([obs4[1:, :, :],obs_next]),dim=0)
buffer.add(obs4.unsqueeze(0), action_onehot.unsqueeze(0), obs4_next.unsqueeze(0), Tensor([reward]).unsqueeze(0), done)
episode_total_reward +=reward
if now_distance <= history_distance:
judge_distance+=1
else:
judge_distance=0
history_distance=max_distance
'''the transition added to buffer
obs4: {ndarray} size (4,84,84)
action: {list} size 6 e.g. [1,0,0,0,0,0] one hot list
obs_next: {ndarray} size (84,84)
reward: {int}
done: {bool}
'''
### go to the next state ###
if done == False:
obs4 = obs4_next
time_step += 1
elif done == True or judge_distance > 50:
env.reset()
obs, _, _, _, _, _, _ = env.step(0)
obs = ob_process(obs)
obs4 = torch.cat(([obs, obs, obs, obs]), dim=0)
episode_num += 1
history_distance = 200
# plot graph #
epi_total_reward_list.append(episode_total_reward)
mean100=np.mean(epi_total_reward_list[-101:-1])
mean_reward_list.append(mean100)
plot_graph(mean_reward_list)
print('episode %d total reward=%.2f'%(episode_num,episode_total_reward))
episode_total_reward = 0
### do one step update ###
if time_step>=OBSERVE and time_step % TRAIN_FREQ == 0:
batch_transition = buffer.sample(BATCH_SIZE)
'''{Transition}
0:{tuple} of {Tensor}-shape-torch.Size([4,84,84])
1:{tuple} of {Tensor}-shape-torch.Size([6])
2:{tuple} of {Tensor}-shape-torch.Size([4,84,84])
3:{tuple} of {int}
4:{tuple} of {bool}
'''
value_net.update(samples=batch_transition, loss_func=LOSS_FUNCTION,
optim_func=OPTIM_METHOD, learn_rate=LEARNING_RATE,
target_net=target_net, BATCH_SIZE=BATCH_SIZE,
GAMMA=GAMMA)
update_times += 1
### copy value net parameters to target net ###
if update_times % NET_COPY_STEP == 0:
target_net.load_state_dict(value_net.state_dict())
torch.save(value_net.state_dict(),PATH)
if __name__=='__main__':
# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
from IPython import display
plt.ion()
learn(env=env.Env(),
MAX_EPISODE=2000000,
EPS_START=0.9,
EPS_END=0.05,
EPS_DECAY=200,
ACTION_NUM=6,
REPLAY_MEMORY_CAPACITY=10000,
BATCH_SIZE=32,
LOSS_FUNCTION=nn.SmoothL1Loss,
OPTIM_METHOD=optim.Adam,
LEARNING_RATE=1e-4,
GAMMA=0.99,
NET_COPY_STEP=1000,
OBSERVE=10000,
TRAIN_FREQ=4,
PATH='net_param.pt'
)