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Update DDPG

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Shangtong Zhang
2018-04-04 21:54:26 -06:00
parent 61a4bcce17
commit 8ad31c79b8
6 changed files with 68 additions and 110 deletions
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component/task.py
+13 -19
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@@ -33,9 +33,6 @@ class BasicTask:
done = (done or self.steps >= self.max_steps)
return next_state, reward, done, info
def random_action(self):
return self.env.action_space.sample()
class ClassicalControl(BasicTask):
def __init__(self, name='CartPole-v0', max_steps=200, log_dir=None):
BasicTask.__init__(self, max_steps)
@@ -100,50 +97,47 @@ class RamAtari(BasicTask):
def normalize_state(self, state):
return np.asarray(state) / 255.0
class ContinuousMountainCar(BasicTask):
name = 'MountainCarContinuous-v0'
success_threshold = 90
def __init__(self, max_steps=sys.maxsize):
BasicTask.__init__(self, max_steps)
self.env = gym.make(self.name)
self.max_episode_steps = self.env._max_episode_steps
self.env._max_episode_steps = sys.maxsize
self.action_dim = self.env.action_space.shape[0]
self.state_dim = self.env.observation_space.shape[0]
class Pendulum(BasicTask):
name = 'Pendulum-v0'
success_threshold = -10
def __init__(self, max_steps=sys.maxsize):
def __init__(self, max_steps=sys.maxsize, log_dir=None):
BasicTask.__init__(self, max_steps)
self.env = gym.make(self.name)
self.action_dim = self.env.action_space.shape[0]
self.state_dim = self.env.observation_space.shape[0]
if log_dir is not None:
mkdir(log_dir)
self.env = Monitor(self.env, '%s/%s' % (log_dir, uuid.uuid1()))
def step(self, action):
return BasicTask.step(self, np.clip(action, -2, 2))
return BasicTask.step(self, np.clip(2 * action, -2, 2))
class Box2DContinuous(BasicTask):
def __init__(self, name, max_steps=sys.maxsize):
def __init__(self, name, max_steps=sys.maxsize, log_dir=None):
BasicTask.__init__(self, max_steps)
self.name = name
self.env = gym.make(self.name)
self.action_dim = self.env.action_space.shape[0]
self.state_dim = self.env.observation_space.shape[0]
if log_dir is not None:
mkdir(log_dir)
self.env = Monitor(self.env, '%s/%s' % (log_dir, uuid.uuid1()))
def step(self, action):
return BasicTask.step(self, np.clip(action, -1, 1))
class Roboschool(BasicTask):
def __init__(self, name, success_threshold=sys.maxsize, max_steps=sys.maxsize):
def __init__(self, name, max_steps=sys.maxsize, log_dir=None):
import roboschool
BasicTask.__init__(self, max_steps)
self.name = name
self.env = gym.make(self.name)
self.action_dim = self.env.action_space.shape[0]
self.state_dim = self.env.observation_space.shape[0]
if log_dir is not None:
mkdir(log_dir)
self.env = Monitor(self.env, '%s/%s' % (log_dir, uuid.uuid1()))
def step(self, action):
return BasicTask.step(self, np.clip(action, -1, 1))