Recent Advances in Reinforcement Learning

Volume 7188 of the series Lecture Notes in Computer Science pp 66-77

Feature Reinforcement Learning in Practice

  • Phuong NguyenAffiliated withCarnegie Mellon UniversityAustralian National UniversityNICTA
  • , Peter SunehagAffiliated withCarnegie Mellon UniversityAustralian National University
  • , Marcus HutterAffiliated withCarnegie Mellon UniversityAustralian National UniversityNICTAETHZ

* Final gross prices may vary according to local VAT.

Get Access


Following a recent surge in using history-based methods for resolving perceptual aliasing in reinforcement learning, we introduce an algorithm based on the feature reinforcement learning framework called ΦMDP [13]. To create a practical algorithm we devise a stochastic search procedure for a class of context trees based on parallel tempering and a specialized proposal distribution. We provide the first empirical evaluation for ΦMDP. Our proposed algorithm achieves superior performance to the classical U-tree algorithm [20] and the recent active-LZ algorithm [6], and is competitive with MC-AIXI-CTW [29] that maintains a bayesian mixture over all context trees up to a chosen depth. We are encouraged by our ability to compete with this sophisticated method using an algorithm that simply picks one single model, and uses Q-learning on the corresponding MDP. Our ΦMDP algorithm is simpler and consumes less time and memory. These results show promise for our future work on attacking more complex and larger problems.