Automated Feature Selection for Inverse Reinforcement Learning

arXiv:2403.15079v1 Announce Type: new
Abstract: Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations. Its use avoids the difficult and tedious procedure of manual reward specification while retaining the generalization power of reinforcement learning. In IRL, the reward is usually represented as a linear combination of features. In continuous state spaces, the state variables alone are not sufficiently rich to be used as features, but which features are good is not known in …

arxiv automated cs.lg cs.ro feature feature selection reinforcement reinforcement learning type