Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO
Summary: arXiv:2604.13517v1 Announce Type: cross
Abstract
Temporal credit assignment in reinforcement learning has long been a central challenge. Inspired by the multi-timescale encoding of the dopamine system in neurobiology, recent research has sought to introduce multiple discount factors into Actor-Critic architectures, such as Proximal Policy Optimization (PPO), to balance short-term responses with long-term planning.
However, this paper reveals that blindly fusing multi-timescale signals in complex delayed-reward tasks can lead to severe algorithmic pathologies. We systematically demonstrate that exposing a temporal attention routing mechanism to policy gradients results in surrogate objective hacking, while adopting gradient-free uncertainty weighting triggers irreversible myopic degeneration, a phenomenon we term the Paradox of Temporal Uncertainty.
Introduction
The challenge of temporal credit assignment in reinforcement learning (RL) has been a focal point for researchers aiming to improve learning efficiency and effectiveness. The recent exploration into multi-timescale approaches draws inspiration from biological systems, particularly the dopamine system, which operates on various timescales to facilitate decision-making.
Challenges in Current Methods
While the integration of multiple discount factors in Actor-Critic architectures like PPO has shown promise, our research uncovers critical drawbacks. Specifically, we identified two major issues:
- Surrogate Objective Hacking: When a temporal attention routing mechanism is employed, it can lead to unexpected manipulations of the learning objectives, undermining the integrity of the learning process.
- Paradox of Temporal Uncertainty: The use of gradient-free uncertainty weighting can cause algorithms to become myopic, disrupting long-term planning and decision-making.
Proposed Target Decoupling Architecture
To tackle these challenges, we propose a novel architecture known as Target Decoupling. This architecture introduces distinct mechanisms for the Critic and Actor components of the PPO framework:
- Critic Side: We maintain multi-timescale predictions to foster auxiliary representation learning, which enhances the understanding of the environment.
- Actor Side: We strictly isolate short-term signals, updating the policy based solely on long-term advantages to avoid conflicts between immediate and delayed rewards.
Empirical Evaluations
Rigorous empirical evaluations were conducted across multiple independent random seeds in the LunarLander-v2 environment. The results demonstrated that our proposed architecture:
- Achieved statistically significant performance improvements.
- Consistently surpassed the “Environment Solved” threshold with minimal variance.
- Completely eliminated instances of policy collapse.
- Successfully escaped local optima traps that often hindered single-timescale baselines.
Conclusion
The findings from this research underscore the importance of careful architectural design in reinforcement learning. Our Target Decoupling architecture not only addresses key pitfalls of current multi-timescale approaches but also sets a new standard for future explorations in the field. By prioritizing representation over routing, we pave the way for more robust and effective RL algorithms.