mirror of
https://github.com/vale981/ray
synced 2025-03-10 05:16:49 -04:00
6724f57b03
* add carla example * add reward * set obs * Sun Dec 17 16:06:00 PST 2017 * add spec * fix measurement * add train script * resize to 80x80 * null * initial small training run * robustify env, clean up action space * clean up vars * switch to town2 which is faster * tunify train.py * add discrete mode * update * fix excessive brakinG * fix the weather * rename * redirect output and from future import * doc * update * fix rebase * allow dqn gpu growht * adjust dqn hyperparams * better ppo parameters
40 lines
1 KiB
Python
40 lines
1 KiB
Python
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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from ray.tune import register_env, run_experiments
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from env import CarlaEnv, ENV_CONFIG
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env_name = "carla_env"
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env_config = ENV_CONFIG.copy()
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env_config.update({
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"x_res": 80,
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"y_res": 80,
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"use_depth_camera": True,
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"discrete_actions": False,
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"max_steps": 150,
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})
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register_env(env_name, lambda: CarlaEnv(env_config))
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run_experiments({
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"carla": {
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"run": "PPO",
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"env": "carla_env",
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"resources": {"cpu": 4, "gpu": 1},
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"config": {
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"num_workers": 1,
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"timesteps_per_batch": 2000,
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"min_steps_per_task": 100,
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"lambda": 0.95,
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"clip_param": 0.2,
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"num_sgd_iter": 20,
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"sgd_stepsize": 0.0001,
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"sgd_batchsize": 32,
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"devices": ["/gpu:0"],
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"tf_session_args": {
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"gpu_options": {"allow_growth": True}
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}
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},
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},
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}, redirect_output=True)
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