Decision Transformer: Reinforcement Learning via Sequence Modeling (Research Paper Explained)

Decision Transformer: Reinforcement Learning via Sequence Modeling (Research Paper Explained)

Jun 10, 2021
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#decisiontransformer #reinforcementlearning #transformer Proper credit assignment over long timespans is a fundamental problem in reinforcement learning. Even methods designed to combat this problem, such as TD-learning, quickly reach their limits when rewards are sparse or noisy. This paper reframes offline reinforcement learning as a pure sequence modeling problem, with the actions being sampled conditioned on the given history and desired future rewards. This allows the authors to use recent advances in sequence modeling using Transformers and achieve competitive results in Offline RL benchmarks. OUTLINE: 0:00 - Intro & Overview 4:15 - Offline Reinforcement Learning 10:10 - Transformers in RL 14:25 - Value Functions and Temporal Difference Learning 20:25 - Sequence Modeling and Reward-to-go 27:20 - Why this is ideal for offline RL 31:30 - The context length problem 34:35 - Toy example: Shortest path from random walks 41:00 - Discount factors 45:50 - Experimental Results 49:25 - Do you need to know the best possible reward? 52:15 - Key-to-door toy experiment 56:00 - Comments & Conclusion Paper: https://arxiv.org/abs/2106.01345 Website: https://sites.google.com/berkeley.edu/decision-transformer Code: https://github.com/kzl/decision-transformer Abstract: We present a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on Atari, OpenAI Gym, and Key-to-Door tasks. Authors: Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ BiliBili: https://space.bilibili.com/1824646584 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/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

0:00 - Intro & Overview 4:15 - Offline Reinforcement Learning 10:10 - Transformers in RL 14:25 - Value Functions and Temporal Difference Learning 20:25 - Sequence Modeling and Reward-to-go 27:20 - Why this is ideal for offline RL 31:30 - The context length problem 34:35 - Toy example: Shortest path from random walks 41:00 - Discount factors 45:50 - Experimental Results 49:25 - Do you need to know the best possible reward? 52:15 - Key-to-door toy experiment 56:00 - Comments & Conclusion
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