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https://github.com/wassname/DeepRL.git
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116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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import gym
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import sys
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import numpy as np
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from atari_wrapper import *
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class BasicTask:
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def __init__(self):
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self.normalized_state = True
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def normalize_state(self, state):
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return state
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def reset(self):
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state = self.env.reset()
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if self.normalized_state:
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return self.normalize_state(state)
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return state
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def step(self, action):
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next_state, reward, done, info = self.env.step(action)
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if self.normalized_state:
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next_state = self.normalize_state(next_state)
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return next_state, np.sign(reward), done, info
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def random_action(self):
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return self.env.action_space.sample()
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class MountainCar(BasicTask):
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name = 'MountainCar-v0'
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success_threshold = -110
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def __init__(self):
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BasicTask.__init__(self)
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self.env = gym.make(self.name)
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self.env._max_episode_steps = sys.maxsize
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class CartPole(BasicTask):
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name = 'CartPole-v0'
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success_threshold = 195
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def __init__(self):
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BasicTask.__init__(self)
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self.env = gym.make(self.name)
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self.env._max_episode_steps = sys.maxsize
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class LunarLander(BasicTask):
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name = 'LunarLander-v2'
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success_threshold = 200
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def __init__(self):
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BasicTask.__init__(self)
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self.env = gym.make(self.name)
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class PixelAtari(BasicTask):
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def __init__(self, name, no_op, frame_skip, normalized_state=True,
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frame_size=84, success_threshold=1000):
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BasicTask.__init__(self)
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self.normalized_state = normalized_state
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self.name = name
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self.success_threshold = success_threshold
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env = gym.make(name)
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assert 'NoFrameskip' in env.spec.id
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env = EpisodicLifeEnv(env)
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env = NoopResetEnv(env, noop_max=no_op)
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env = MaxAndSkipEnv(env, skip=frame_skip)
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if 'FIRE' in env.unwrapped.get_action_meanings():
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env = FireResetEnv(env)
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env = ProcessFrame(env, frame_size)
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self.env = ClippedRewardsWrapper(env)
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def normalize_state(self, state):
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return np.asarray(state, dtype=np.float32) / 255.0
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class Pendulum(BasicTask):
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name = 'Pendulum-v0'
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success_threshold = -10
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def __init__(self):
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BasicTask.__init__(self)
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self.env = gym.make(self.name)
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self.env._max_episode_steps = sys.maxsize
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self.action_dim = self.env.action_space.shape[0]
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self.state_dim = self.env.observation_space.shape[0]
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def normalize_state(self, state):
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state = (state - self.env.observation_space.low) / \
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(self.env.observation_space.high - self.env.observation_space.low)
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state = state * 2 - 1
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return state
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def step(self, action):
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action = np.clip(action, -2, 2)
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next_state, reward, done, info = self.env.step(action)
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return self.normalize_state(next_state), reward, done, info
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class BipedalWalker(BasicTask):
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name = 'BipedalWalker-v2'
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success_threshold = 300
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def __init__(self):
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BasicTask.__init__(self)
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self.env = gym.make(self.name)
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self.env._max_episode_steps = sys.maxsize
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self.action_dim = self.env.action_space.shape[0]
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self.state_dim = self.env.observation_space.shape[0]
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def step(self, action):
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action = np.clip(action, -1, 1)
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next_state, reward, done, info = self.env.step(action)
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return next_state, reward, done, info
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