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Eric Liang
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* wip * lint * wip * up * wip * update examples * wip * remove carla * update * improve envspec * link to custom * Update rllib-env.rst * update * fix * fn * lint * ds * ssd games * desc * fix up docs * fix
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
"""Example of a custom gym environment. Run this for a demo.
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This example shows:
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- using a custom environment
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- using Tune for grid search
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You can visualize experiment results in ~/ray_results using TensorBoard.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import gym
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from gym.spaces import Discrete, Box
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import ray
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from ray.tune import run_experiments, grid_search
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class SimpleCorridor(gym.Env):
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"""Example of a custom env in which you have to walk down a corridor.
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You can configure the length of the corridor via the env config."""
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def __init__(self, config):
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self.end_pos = config["corridor_length"]
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self.cur_pos = 0
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self.action_space = Discrete(2)
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self.observation_space = Box(
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0.0, self.end_pos, shape=(1, ), dtype=np.float32)
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def reset(self):
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self.cur_pos = 0
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return [self.cur_pos]
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def step(self, action):
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assert action in [0, 1], action
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if action == 0 and self.cur_pos > 0:
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self.cur_pos -= 1
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elif action == 1:
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self.cur_pos += 1
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done = self.cur_pos >= self.end_pos
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return [self.cur_pos], 1 if done else 0, done, {}
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if __name__ == "__main__":
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# Can also register the env creator function explicitly with:
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# register_env("corridor", lambda config: SimpleCorridor(config))
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ray.init()
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run_experiments({
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"demo": {
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"run": "PPO",
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"env": SimpleCorridor, # or "corridor" if registered above
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"stop": {
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"timesteps_total": 10000,
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},
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"config": {
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"lr": grid_search([1e-2, 1e-4, 1e-6]), # try different lrs
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"num_workers": 1, # parallelism
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"env_config": {
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"corridor_length": 5,
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},
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},
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},
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})
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