ray/examples/carla/train_ppo.py
Eric Liang 0ae660ce4e [carla] In carla example, save all images and measurements to local disk (#1350)
* revamp saving

* smaller jpgs

* hide verbose

* Tue Dec 19 22:25:01 PST 2017

* make sure temp dirs sort lexiographically

* save total reward too

* zero pad i

* 160x160 dqn

* ever higher res dqn
2017-12-21 15:19:55 -08:00

41 lines
1 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ray.tune import register_env, run_experiments
from env import CarlaEnv, ENV_CONFIG
env_name = "carla_env"
env_config = ENV_CONFIG.copy()
env_config.update({
"verbose": False,
"x_res": 80,
"y_res": 80,
"use_depth_camera": True,
"discrete_actions": False,
"max_steps": 150,
})
register_env(env_name, lambda: CarlaEnv(env_config))
run_experiments({
"carla": {
"run": "PPO",
"env": "carla_env",
"resources": {"cpu": 4, "gpu": 1},
"config": {
"num_workers": 1,
"timesteps_per_batch": 2000,
"min_steps_per_task": 100,
"lambda": 0.95,
"clip_param": 0.2,
"num_sgd_iter": 20,
"sgd_stepsize": 0.0001,
"sgd_batchsize": 32,
"devices": ["/gpu:0"],
"tf_session_args": {
"gpu_options": {"allow_growth": True}
}
},
},
}, redirect_output=True)