5 accepted papers
In this paper, we highlight the field of explainable sequential decision making. We discuss how the problem of explaining sequential decisions gives rise to problems and challenges that are absent from scenarios that focus on explaining single-shot decision making. We provide a short survey of some
Outside of transfer learning settings, reinforcement learning agents start their learning process from a clean slate. As a result, such agents have to go through a slow process to learn even the most obvious skills required to solve a problem. In this paper, we present InnateCoder, a system that lev
Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We…
How can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time. T…
This paper addresses the challenge of online generalization in tree search. We propose Multiple Estimator Monte Carlo Tree Search (ME-MCTS), with a two-fold contribution: first, we introduce a formalization of online generalization that can represent existing techniques such as "history heuristics",…