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AI Research
arXiv CS.AI·May 25·5 min read
Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO
TL;DR
Reinforcement learning faces challenges in assigning credit over time, particularly in complex tasks. This paper introduces a Target Decoupling architecture that separates short-term and long-term signals in Proximal Policy Optimization (PPO).
✦ Why It Matters
Engineers can implement the Target Decoupling architecture to improve reinforcement learning model stability and performance.
Key Takeaways
- Implement Target Decoupling to enhance learning stability.
- Use multi-timescale predictions for better representation learning.
- Avoid hyperparameter adjustments for consistent performance.
- Test in environments like LunarLander-v2 for validation.
Full Summary
Temporal credit assignment is a key challenge in reinforcement learning, where agents must learn to associate actions with rewards over varying time scales. Inspired by the dopamine system in neurobiology, researchers have attempted to integrate multiple discount factors into Actor-Critic architectures like Proximal Policy Optimization (PPO).
However, this paper identifies issues such as surrogate objective hacking and myopic degeneration when using multi-timescale signals. To address these, a Target Decoupling architecture is proposed, which maintains multi-timescale predictions for auxiliary representation learning on the Critic side while isolating short-term signals for policy updates on the Actor side.
Rigorous testing in the LunarLander-v2 environment demonstrated that this approach consistently achieved statistically significant performance improvements, surpassing the 'Environment Solved' threshold with minimal variance and eliminating policy collapse. These findings suggest that separating temporal signals can enhance learning stability and efficiency in reinforcement learning tasks.