Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
In this paper, we present a novel method to adapt the temporal radio maps for indoor location determination by offsetting the variational environmental factors using data mining t...
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Grassmannian beamforming is an efficient way to quantize channel state information in multiple-input multiple-output wireless systems. Unfortunately, multiuser systems require la...