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.
Hybrid Reward Architecture for Reinforcement Learning
H. V. Seijen,Mehdi Fatemi,R. Laroche,Joshua Romoff,Tavian Barnes,Jeffrey Tsang
Published 2017 in Neural Information Processing Systems
ABSTRACT
PUBLICATION RECORD
- Publication year
2017
- Venue
Neural Information Processing Systems
- Publication date
2017-06-13
- Fields of study
Mathematics, Computer Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-31 of 31 references · Page 1 of 1