Abstract: Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games. We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We shall review what goes into this agent, and how it achieves this level of performance.


Bio: Charles Blundell is a research scientist at DeepMind where he leads a team who have recently been working on exploration in reinforcement learning and combining episodic memory with deep learning. He holds a PhD in machine learning from the Gatsby Unit at UCL.

Q&A Facilitator: Sam Ritter


References:
https://arxiv.org/abs/2003.13350
https://arxiv.org/abs/2002.06038
https://deepmind.com/blog/article/Age...

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