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"""Example of a custom gym environment and model. Run this for a demo.
This example shows:
- using a custom environment
- using a custom model
- using Tune for grid search
You can visualize experiment results in ~/ray_results using TensorBoard.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import gym
from ray.rllib.models import FullyConnectedNetwork, Model, ModelCatalog
from gym.spaces import Discrete, Box
import ray
from ray import tune
from ray.tune import grid_search
class SimpleCorridor(gym.Env):
"""Example of a custom env in which you have to walk down a corridor.
You can configure the length of the corridor via the env config."""
def __init__(self, config):
self.end_pos = config["corridor_length"]
self.cur_pos = 0
self.action_space = Discrete(2)
self.observation_space = Box(
0.0, self.end_pos, shape=(1, ), dtype=np.float32)
def reset(self):
self.cur_pos = 0
return [self.cur_pos]
def step(self, action):
assert action in [0, 1], action
if action == 0 and self.cur_pos > 0:
self.cur_pos -= 1
elif action == 1:
self.cur_pos += 1
done = self.cur_pos >= self.end_pos
return [self.cur_pos], 1 if done else 0, done, {}
class CustomModel(Model):
"""Example of a custom model.
This model just delegates to the built-in fcnet.
def _build_layers_v2(self, input_dict, num_outputs, options):
self.obs_in = input_dict["obs"]
self.fcnet = FullyConnectedNetwork(input_dict, self.obs_space,
self.action_space, num_outputs,
return self.fcnet.outputs, self.fcnet.last_layer
if __name__ == "__main__":
# Can also register the env creator function explicitly with:
# register_env("corridor", lambda config: SimpleCorridor(config))
ModelCatalog.register_custom_model("my_model", CustomModel)
"timesteps_total": 10000,
"env": SimpleCorridor, # or "corridor" if registered above
"model": {
"custom_model": "my_model",
"lr": grid_search([1e-2, 1e-4, 1e-6]), # try different lrs
"num_workers": 1, # parallelism
"env_config": {
"corridor_length": 5,
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