Yannic Kilcher
#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
Trajectory Transformer: https://trajectory-transformer.github.io/
Upside-Down RL: https://arxiv.org/abs/1912.02875
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
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