Open access
Sep 2026
Temporal Expert-Based Reward Learning for Inverse Reinforcement Learning
Learning reward functions from expert demonstrations removes the need for manual reward engineering in reinforcement learning applied to robotic manipulation. Existing methods, however, require trajectory quality annotations, episode success labels, or produce implicit rewards that are difficult to inspect. This paper...
Francisco José Naranjo Campos, Juan G. Victores, Almudena Alcaide et al.
· Jornadas de Automática · 0 citations