44 citations · 172 across the 14 of their papers we have counts for
15 papers
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents
Adrienne Tuynman, Ronald Ortner
We present an approach for the quantification of the usefulness of transfer in reinforcement learning via regret bounds for a multi-agent setting. Considering a number of …
Autonomous exploration for navigating in non-stationary CMPs
Pratik Gajane, Ronald Ortner, Peter Auer +1
We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change. For this setting, we p…
Variational Regret Bounds for Reinforcement Learning
Pratik Gajane, Ronald Ortner, Peter Auer
We consider undiscounted reinforcement learning in Markov decision processes (MDPs) where both the reward functions and the state-transition probabilities may vary (gradually or ab…
Regret Bounds for Reinforcement Learning via Markov Chain Concentration
Ronald Ortner
We give a simple optimistic algorithm for which it is easy to derive regret bounds of after steps in uniformly ergodic Markov decision proce…
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions
Pratik Gajane, Ronald Ortner, Peter Auer
We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time. For this problem…
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Ronan Fruit, Matteo Pirotta, Alessandro Lazaric +1
We introduce SCAL, an algorithm designed to perform efficient exploration-exploitation in any unknown weakly-communicating Markov decision process (MDP) for which an upper bound $c…