NDSS 2025 – Reinforcement Unlearning

This NDSS session overview presents research on reinforcement unlearning for reinforcement learning agents, addressing how to revoke the influence of entire training environments while preserving performance elsewhere. The authors propose two approaches—gradual knowledge erasure via decremental reinforcement learning and targeted environment poisoning to overwrite prior learning—and introduce an 'environment inference' method to assess unlearning effectiveness.