7 papers
Optimized Look-Ahead Tree Policies: A Bridge Between Look-Ahead Tree Policies and Direct Policy Search
Tobias Jung, Louis Wehenkel, Damien Ernst +1
Direct policy search (DPS) and look-ahead tree (LT) policies are two widely used classes of techniques to produce high performance policies for sequential decision-making problems.…
Learning RoboCup-Keepaway with Kernels
Tobias Jung, Daniel Polani
We apply kernel-based methods to solve the difficult reinforcement learning problem of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the high-dimensiona…
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Tobias Jung, Peter Stone
Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of the main cha…
Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-like Exploration
Tobias Jung, Peter Stone
We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achieve low sa…
Empowerment for Continuous Agent-Environment Systems
Tobias Jung, Daniel Polani, Peter Stone
This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the effic…
Outbound SPIT Filter with Optimal Performance Guarantees
Tobias Jung, Sylvain Martin, Mohamed Nassar +2
This paper presents a formal framework for identifying and filtering SPIT calls (SPam in Internet Telephony) in an outbound scenario with provable optimal performance. In so doing,…