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* wip * Sat Dec 30 15:07:28 PST 2017 * log video * video doesn't work well * scenario integration * Sat Dec 30 17:30:22 PST 2017 * Sat Dec 30 17:31:05 PST 2017 * Sat Dec 30 17:31:32 PST 2017 * Sat Dec 30 17:32:16 PST 2017 * Sat Dec 30 17:34:11 PST 2017 * Sat Dec 30 17:34:50 PST 2017 * Sat Dec 30 17:35:34 PST 2017 * Sat Dec 30 17:38:49 PST 2017 * Sat Dec 30 17:40:39 PST 2017 * Sat Dec 30 17:43:00 PST 2017 * Sat Dec 30 17:43:04 PST 2017 * Sat Dec 30 17:45:56 PST 2017 * Sat Dec 30 17:46:26 PST 2017 * Sat Dec 30 17:47:02 PST 2017 * Sat Dec 30 17:51:53 PST 2017 * Sat Dec 30 17:52:54 PST 2017 * Sat Dec 30 17:56:43 PST 2017 * Sat Dec 30 18:27:07 PST 2017 * Sat Dec 30 18:27:52 PST 2017 * fix train * Sat Dec 30 18:41:51 PST 2017 * Sat Dec 30 18:54:11 PST 2017 * Sat Dec 30 18:56:22 PST 2017 * Sat Dec 30 19:05:04 PST 2017 * Sat Dec 30 19:05:23 PST 2017 * Sat Dec 30 19:11:53 PST 2017 * Sat Dec 30 19:14:31 PST 2017 * Sat Dec 30 19:16:20 PST 2017 * Sat Dec 30 19:18:05 PST 2017 * Sat Dec 30 19:18:45 PST 2017 * Sat Dec 30 19:22:44 PST 2017 * Sat Dec 30 19:24:41 PST 2017 * Sat Dec 30 19:26:57 PST 2017 * Sat Dec 30 19:40:37 PST 2017 * wip models * reward bonus * test prep * Sun Dec 31 18:45:25 PST 2017 * Sun Dec 31 18:58:28 PST 2017 * Sun Dec 31 18:59:34 PST 2017 * Sun Dec 31 19:03:33 PST 2017 * Sun Dec 31 19:05:05 PST 2017 * Sun Dec 31 19:09:25 PST 2017 * fix train * kill * add tuple preprocessor * Sun Dec 31 20:38:33 PST 2017 * Sun Dec 31 22:51:24 PST 2017 * Sun Dec 31 23:14:13 PST 2017 * Sun Dec 31 23:16:04 PST 2017 * Mon Jan 1 00:08:35 PST 2018 * Mon Jan 1 00:10:48 PST 2018 * Mon Jan 1 01:08:31 PST 2018 * Mon Jan 1 14:45:44 PST 2018 * Mon Jan 1 14:54:56 PST 2018 * Mon Jan 1 17:29:29 PST 2018 * switch to euclidean dists * Mon Jan 1 17:39:27 PST 2018 * Mon Jan 1 17:41:47 PST 2018 * Mon Jan 1 17:44:18 PST 2018 * Mon Jan 1 17:47:09 PST 2018 * Mon Jan 1 20:31:02 PST 2018 * Mon Jan 1 20:39:33 PST 2018 * Mon Jan 1 20:40:55 PST 2018 * Mon Jan 1 20:55:06 PST 2018 * Mon Jan 1 21:05:52 PST 2018 * fix env path * merge richards fix * fix hash * Mon Jan 1 22:04:00 PST 2018 * Mon Jan 1 22:25:29 PST 2018 * Mon Jan 1 22:30:42 PST 2018 * simplified reward function * add framestack * add env configs * simplify speed reward * Tue Jan 2 17:36:15 PST 2018 * Tue Jan 2 17:49:16 PST 2018 * Tue Jan 2 18:10:38 PST 2018 * add lane keeping simple mode * Tue Jan 2 20:25:26 PST 2018 * Tue Jan 2 20:30:30 PST 2018 * Tue Jan 2 20:33:26 PST 2018 * Tue Jan 2 20:41:42 PST 2018 * ppo lane keep * simplify discrete actions * Tue Jan 2 21:41:05 PST 2018 * Tue Jan 2 21:49:03 PST 2018 * Tue Jan 2 22:12:23 PST 2018 * Tue Jan 2 22:14:42 PST 2018 * Tue Jan 2 22:20:59 PST 2018 * Tue Jan 2 22:23:43 PST 2018 * Tue Jan 2 22:26:27 PST 2018 * Tue Jan 2 22:27:20 PST 2018 * Tue Jan 2 22:44:00 PST 2018 * Tue Jan 2 22:57:58 PST 2018 * Tue Jan 2 23:08:51 PST 2018 * Tue Jan 2 23:11:32 PST 2018 * update dqn reward * Thu Jan 4 12:29:40 PST 2018 * Thu Jan 4 12:30:26 PST 2018 * Update train_dqn.py * fix
53 lines
1.5 KiB
Python
53 lines
1.5 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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import ray
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from ray.tune import grid_search, register_env, run_experiments
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from env import CarlaEnv, ENV_CONFIG
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from models import register_carla_model
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from scenarios import TOWN2_STRAIGHT
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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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"verbose": False,
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"x_res": 80,
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"y_res": 80,
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"squash_action_logits": grid_search([False, True]),
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"use_depth_camera": False,
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"discrete_actions": False,
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"server_map": "/Game/Maps/Town02",
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"reward_function": grid_search(["custom", "corl2017"]),
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"scenarios": TOWN2_STRAIGHT,
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})
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register_env(env_name, lambda env_config: CarlaEnv(env_config))
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register_carla_model()
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redis_address = ray.services.get_node_ip_address() + ":6379"
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run_experiments({
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"carla-a3c": {
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"run": "A3C",
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"env": "carla_env",
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"resources": {"cpu": 5, "gpu": 2, "driver_gpu_limit": 0},
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"config": {
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"env_config": env_config,
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"use_gpu_for_workers": True,
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"model": {
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"custom_model": "carla",
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"custom_options": {
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"image_shape": [80, 80, 6],
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},
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"conv_filters": [
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[16, [8, 8], 4],
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[32, [4, 4], 2],
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[512, [10, 10], 1],
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],
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},
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"gamma": 0.95,
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"num_workers": 2,
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},
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},
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}, redis_address=redis_address)
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