Kalman Temporal Differences
This addresses scalability issues in reinforcement learning for practitioners, though it appears incremental as it builds on existing temporal difference methods.
The paper tackles the scalability problem in reinforcement learning by introducing the Kalman Temporal Differences (KTD) framework, which offers sample-efficiency, non-linear approximation, non-stationarity handling, and uncertainty management, and shows that related algorithms outperform state-of-the-art methods on classical benchmarks.
Because reinforcement learning suffers from a lack of scalability, online value (and Q-) function approximation has received increasing interest this last decade. This contribution introduces a novel approximation scheme, namely the Kalman Temporal Differences (KTD) framework, that exhibits the following features: sample-efficiency, non-linear approximation, non-stationarity handling and uncertainty management. A first KTD-based algorithm is provided for deterministic Markov Decision Processes (MDP) which produces biased estimates in the case of stochastic transitions. Than the eXtended KTD framework (XKTD), solving stochastic MDP, is described. Convergence is analyzed for special cases for both deterministic and stochastic transitions. Related algorithms are experimented on classical benchmarks. They compare favorably to the state of the art while exhibiting the announced features.