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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
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711 B
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14 lines
711 B
Text
(Experimental) OpenAI gym environment for https://github.com/carla-simulator/carla
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To run, first download and unpack the Carla binaries from this URL: https://github.com/carla-simulator/carla/releases/tag/0.7.0
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Note that currently you also need to clone the Python code from `carla/benchmark_branch` which includes the Carla planner.
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Then, you can try running env.py to drive the car. Run one of the train_* scripts to attempt training.
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$ pkill -9 Carla
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$ export CARLA_SERVER=/PATH/TO/CARLA_0.7.0/CarlaUE4.sh
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$ export CARLA_PY_PATH=/PATH/TO/CARLA_BENCHMARK_BRANCH_REPO/PythonClient
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$ python env.py
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Check out the scenarios.py file for different training and test scenarios that can be used.
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