Files
DeepRL/task.py
T

57 lines
1.6 KiB
Python

#######################################################################
# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
# Permission given to modify the code as long as you keep this #
# declaration at the top #
#######################################################################
import gym
import sys
import numpy as np
import cv2
class BasicTask:
def transfer_state(self, state):
return state
def reset(self):
return self.transfer_state(self.env.reset())
def step(self, action):
next_state, reward, done, info = self.env.step(action)
next_state = self.transfer_state(next_state)
return next_state, np.sign(reward), done, info
class MountainCar(BasicTask):
name = 'MountainCar-v0'
success_threshold = -110
def __init__(self):
self.env = gym.make(self.name)
self.env._max_episode_steps = sys.maxsize
class CartPole(BasicTask):
name = 'CartPole-v0'
success_threshold = 195
def __init__(self):
self.env = gym.make(self.name)
class LunarLander(BasicTask):
name = 'LunarLander-v2'
success_threshold = 200
def __init__(self):
self.env = gym.make(self.name)
class PixelAtari(BasicTask):
width = 84
height = 84
success_threshold = 1000
def __init__(self, name):
self.env = gym.make(name)
def transfer_state(self, state):
img = (state[:, :, 0] * 0.299 + state[:, :, 1] * 0.587 + state[:, :, 2] * 0.114) / 255.0
img = cv2.resize(img, (self.width, self.height))
return np.reshape(img, (1, self.width, self.height))