We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an age...
KLAIM is an experimental language designed for modeling and programming distributed systems composed of mobile components where distribution awareness and dynamic system architect...
We present a dynamic programming approach for the solution of first-order Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the ...
We study the computational complexity of basic decision problems for one-counter simple stochastic games (OC-SSGs), under various objectives. OC-SSGs are 2-player turn-based stoch...