Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO
Researchers have developed a new architecture called Target Decoupling to address issues in multi-timescale reinforcement learning. This approach separates short-term and long-term signals to improve policy updates, preventing common problems like surrogate objective hacking and policy collapse. Experiments on the LunarLander-v2 environment showed significant performance gains and reduced variance compared to existing methods. AI
IMPACT Introduces a novel architecture that enhances performance and stability in reinforcement learning tasks.