What Matters In OnPolicy Reinforcement Learning A LargeScale Empirical Study Paper Explained

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#ai #research #machinelearning • Online Reinforcement Learning is a flourishing field with countless methods for practitioners to choose from. However, each of those methods comes with a plethora of hyperparameter choices. This paper builds a unified framework for five continuous control tasks and investigates in a large-scale study the effects of these choices. As a result, they come up with a set of recommendations for future research and applications. • OUTLINE: • 0:00 - Intro Overview • 3:55 - Parameterized Agents • 7:00 - Unified Online RL and Parameter Choices • 14:10 - Policy Loss • 16:40 - Network Architecture • 20:25 - Initial Policy • 24:20 - Normalization Clipping • 26:30 - Advantage Estimation • 28:55 - Training Setup • 33:05 - Timestep Handling • 34:10 - Optimizers • 35:05 - Regularization • 36:10 - Conclusion Comments • Paper: https://arxiv.org/abs/2006.05990 • Abstract: • In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress (Engstrom'20). As a step towards filling that gap, we implement over 50 such choices in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents. • Authors: Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, Olivier Bachem • • Links: • YouTube: / yannickilcher • Twitter: / ykilcher • Discord: / discord • BitChute: https://www.bitchute.com/channel/yann... • Minds: https://www.minds.com/ykilcher • Parler: https://parler.com/profile/YannicKilcher • LinkedIn: / yannic-kilcher-488534136 • If you want to support me, the best thing to do is to share out the content :) • If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): • SubscribeStar: https://www.subscribestar.com/yannick... • Patreon: / yannickilcher • Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq • Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 • Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m • Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

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