def __init__(self):
super(PredictActionsCartpoleEnv, self).__init__()
self.cartpole = CartPoleEnv()
self.observation_space = self.cartpole.observation_space
self.action_space = spaces.Tuple(
(self.cartpole.action_space, ) * (NUM_PREDICTED_ACTIONS + 1))
def __init__(self):
super(PredictObsCartpoleEnv, self).__init__()
self.cartpole = CartPoleEnv()
self.observation_space = self.cartpole.observation_space
self.action_space = spaces.Tuple((self.cartpole.action_space, ) +
(self.cartpole.observation_space, ) *
(NUM_PREDICTED_OBSERVATIONS))
def cartpole_env(env_id=1, **kwargs):
return GymEnvWrapper(CartPoleEnv(**kwargs), act_null_value=0)
import gym
import numpy as np
import math
from gym.envs.classic_control.cartpole import CartPoleEnv
from envs.task import Task
_env = CartPoleEnv()
_X_THRESHOLD = _env.x_threshold
_THETA_THRESHOLD = _env.theta_threshold_radians
del _env
class CartPoleBalanceTask(Task):
def __call__(self, states, actions, next_states):
next_dones = GYMMB_ContinuousCartPole.is_done(next_states)
dones = GYMMB_ContinuousCartPole.is_done(states)
rewards = 1.0 - next_dones.float() * dones.float() # basically, you get always 1.0 unless you exceed the is_done termination criteria
return rewards
class CartPoleSpeedyBalanceTask(Task):
def __call__(self, states, actions, next_states):
rewards = states[:, 1].abs() # more rewards for abs velocity. No free points
return rewards
class GYMMB_ContinuousCartPole(CartPoleEnv):
"""
A continuous version.
Observation space:
logging.debug("actions: %s\n%s", action_tensor.shape, action_tensor)
logging.debug("log_prob: %s\n%s", log_prob_tensor.shape,
log_prob_tensor)
logging.debug("log_prob test: %s\n%s", prob_tensor.shape,
torch.log(prob_tensor))
logging.debug("rewards: %s\n%s", rewards_tensor.shape, rewards_tensor)
logging.debug("loss: %s\n%s", loss.shape, loss)
if __name__ == "__main__":
#Create environment. Note that make actually wraps the actual environment.
#The wrapper will end an episode based on a time limit
# env = gym.make("CartPole-v0") #state = [x,x_dot,theta,theta_dot], actions = [left_or_right] 0 = left 1 = right
env = CartPoleEnv() #instanciate the env directly, without the wrapper
#get the state and action size from the environement
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
#instanciate agent
agent = PolicyGradientAgent(state_size, action_size)
done = True
last_render_time = time.time()
episode_count = -1
#forever
while True:
#if episode is done
