Abstract In this paper we address the problem of simultaneous learning and coordination in multiagent Markov decision problems (MMDPs) with infinite state-spaces. We separate this ...
In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of serv...
W. T. Luke Teacy, Georgios Chalkiadakis, Alex Roge...
With the goal to generate more scalable algorithms with higher efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towards combining classical tec...
Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...