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kengz/SLM-Lab: Discrete SAC benchmark update

Authors: Wah Loon Keng; Graesser, Laura; Allan-Avatar1; Snyk Bot; Gillen, Sean; Rahim16; Cvitkovic, Milan; +1 Authors

kengz/SLM-Lab: Discrete SAC benchmark update

Abstract

Discrete SAC benchmark update Upload PR #429 Dropbox data Env. \ Alg. DQN DDQN+PER A2C (GAE) A2C (n-step) PPO SAC Breakout <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737546-dabb6380-f9c8-11e9-901e-b96cc28f1fdf.png"></details> 80.88 182 377 398 443 3.51* Pong <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737554-e018ae00-f9c8-11e9-92b5-3bd8d213b1e0.png"></details> 18.48 20.5 19.31 19.56 20.58 19.87* Seaquest <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737557-e3139e80-f9c8-11e9-9446-119593ca956b.png"></details> 1185 4405 1070 1684 1715 171* Qbert <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737559-e575f880-f9c8-11e9-8c98-f14c82041a45.png"></details> 5494 11426 12405 13590 13460 923* LunarLander <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737566-e7d85280-f9c8-11e9-8df8-39c1205c5308.png"></details> 192 233 25.21 68.23 214 276 UnityHallway <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737569-ead34300-f9c8-11e9-9e26-61fe1d779989.png"></details> -0.32 0.27 0.08 -0.96 0.73 0.01 UnityPushBlock <details><summary><i>graph</i></summary><img src="https://user-images.githubusercontent.com/8209263/67737577-eeff6080-f9c8-11e9-931c-843ba697779c.png"></details> 4.88 4.93 4.68 4.93 4.97 -0.70 Episode score at the end of training attained by SLM Lab implementations on discrete-action control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. A Random baseline with score averaged over 100 episodes is included. Results marked with * were trained using the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time. For SAC, Breakout, Pong and Seaquest were trained for 2M frames instead of 10M frames. For the full Atari benchmark, see Atari Benchmark

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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