mirror of
https://github.com/wassname/ray.git
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eugenevinitsky
37076a9ff8
* working multi action distribution and multiagent model * currently working but the splits arent done in the right place * added shared models * added categorical support and mountain car example * now compatible with generalized advantage estimation * working multiagent code with discrete and continuous example * moved reshaper to utils * code review changes made, ppo action placeholder moved to model catalog, all multiagent code moved out of fcnet * added examples in * added PEP8 compliance * examples are mostly pep8 compliant * removed all flake errors * added examples to jenkins tests * fixed custom options bug * added lines to let docker file find multiagent tests * shortened example run length * corrected nits * fixed flake errors
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
from gym.spaces import Box, Tuple
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from gym.utils import seeding
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from gym.envs.classic_control.pendulum import PendulumEnv
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import numpy as np
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"""
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Multiagent pendulum that sums its torques to generate an action
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"""
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class MultiAgentPendulumEnv(PendulumEnv):
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metadata = {
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'render.modes': ['human', 'rgb_array'],
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'video.frames_per_second': 30
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}
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def __init__(self):
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self.max_speed = 8
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self.max_torque = 2.
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self.dt = .05
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self.viewer = None
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high = np.array([1., 1., self.max_speed])
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self.action_space = [Box(low=-self.max_torque / 2,
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high=self.max_torque / 2, shape=(1,))
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for _ in range(2)]
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self.observation_space = Tuple(tuple(Box(low=-high, high=high)
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for _ in range(2)))
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self._seed()
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def _seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def _step(self, u):
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th, thdot = self.state # th := theta
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summed_u = np.sum(u)
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g = 10.
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m = 1.
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length = 1.
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dt = self.dt
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summed_u = np.clip(summed_u, -self.max_torque, self.max_torque)
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self.last_u = summed_u # for rendering
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costs = self.angle_normalize(th) ** 2 + .1 * thdot ** 2 + \
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.001 * (summed_u ** 2)
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newthdot = thdot + (-3 * g / (2 * length) * np.sin(th + np.pi) +
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3. / (m * length ** 2) * summed_u) * dt
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newth = th + newthdot * dt
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newthdot = np.clip(newthdot, -self.max_speed, self.max_speed)
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self.state = np.array([newth, newthdot])
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return self._get_obs(), -costs, False, {}
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def _reset(self):
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high = np.array([np.pi, 1])
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self.state = self.np_random.uniform(low=-high, high=high)
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self.last_u = None
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return self._get_obs()
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def _get_obs(self):
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theta, thetadot = self.state
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return [np.array([np.cos(theta), np.sin(theta), thetadot])
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for _ in range(2)]
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def angle_normalize(self, x):
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return (((x + np.pi) % (2 * np.pi)) - np.pi)
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