Relational Deep Reinforcement Learning
Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia
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ReproduceCode
- github.com/nicoladainese96/SC2-RLpytorch★ 6
- github.com/mavischer/DRRLpytorch★ 0
- github.com/Liberty3000/rlnone★ 0
- github.com/nicoladainese96/RelationalDeepRLpytorch★ 0
- github.com/nathanin/padtf★ 0
- github.com/inoryy/reavertf★ 0
- github.com/nathangrinsztajn/Box-Worldnone★ 0
Abstract
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.