Continuous Environment Benchmark

🥇 Continuous Environment Benchmark Result

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Env. \ Alg.	A2C (GAE)	A2C (n-step)	PPO	SAC
RoboschoolAnt	787	1396	1843	2915
RoboschoolAtlasForwardWalk	59.87	88.04	172	800
RoboschoolHalfCheetah	712	439	1960	2497
RoboschoolHopper	710	285	2042	2045
RoboschoolInvertedDoublePendulum	996	4410	8076	8085
RoboschoolInvertedPendulum	995	978	986	941
RoboschoolReacher	12.9	10.16	19.51	19.99
RoboschoolWalker2d	280	220	1660	1894
RoboschoolHumanoid	99.31	54.58	2388	2621*
RoboschoolHumanoidFlagrun	73.57	178	2014	2056*
RoboschoolHumanoidFlagrunHarder	-429	253	680	280*
Unity3DBall	33.48	53.46	78.24	98.44
Unity3DBallHard	62.92	71.92	91.41	97.06

Episode score at the end of training attained by SLM Lab implementations on continuous control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. Results marked with * require 50M-100M frames, so we use the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time.

📈 Continuous Environment Benchmark Result Plots

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