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Hybrid Reward Architecture for Reinforcement Learning
Harm Van Seijen · Mehdi Fatemi · Romain Laroche · Joshua Romoff · Tavian Barnes · Jeffrey Tsang

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #200

One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dimensional representation using a deep network. While this approach works well in many domains, in domains where the optimal value function cannot easily be reduced to a low-dimensional representation, learning can be very slow and unstable. This paper contributes towards tackling such challenging domains, by proposing a new method, called Hybrid Reward Architecture (HRA). HRA takes as input a decomposed reward function and learns a separate value function for each component reward function. Because each component typically only depends on a subset of all features, the corresponding value function can be approximated more easily by a low-dimensional representation, enabling more effective learning. We demonstrate HRA on a toy-problem and the Atari game Ms. Pac-Man, where HRA achieves above-human performance.

Author Information

Harm Van Seijen (Microsoft Research)
Mehdi Fatemi (Microsoft)
Romain Laroche (Microsoft Research)
Joshua Romoff (McGill University)
Tavian Barnes (Microsoft)
Jeffrey Tsang

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