ray/rllib/examples/env/pendulum_mass.py

29 lines
783 B
Python

import numpy as np
import gym
from gym.envs.classic_control.pendulum import PendulumEnv
from ray.rllib.env.meta_env import MetaEnv
class PendulumMassEnv(PendulumEnv, gym.utils.EzPickle, MetaEnv):
"""PendulumMassEnv varies the weight of the pendulum
Tasks are defined to be weight uniformly sampled between [0.5,2]
"""
def sample_tasks(self, n_tasks):
# Mass is a random float between 0.5 and 2
return np.random.uniform(low=0.5, high=2.0, size=(n_tasks, ))
def set_task(self, task):
"""
Args:
task: task of the meta-learning environment
"""
self.m = task
def get_task(self):
"""
Returns:
task: task of the meta-learning environment
"""
return self.m