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No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO (2405.00662v3)

Published 1 May 2024 in cs.LG

Abstract: Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fitting new targets. However, previous works have observed that networks trained under non-stationarity exhibit an inability to continue learning, termed loss of plasticity, and eventually a collapse in performance. For off-policy deep value-based RL methods, this phenomenon has been correlated with a decrease in representation rank and the ability to fit random targets, termed capacity loss. Although this correlation has generally been attributed to neural network learning under non-stationarity, the connection to representation dynamics has not been carefully studied in on-policy policy optimization methods. In this work, we empirically study representation dynamics in Proximal Policy Optimization (PPO) on the Atari and MuJoCo environments, revealing that PPO agents are also affected by feature rank deterioration and capacity loss. We show that this is aggravated by stronger non-stationarity, ultimately driving the actor's performance to collapse, regardless of the performance of the critic. We ask why the trust region, specific to methods like PPO, cannot alleviate or prevent the collapse and find a connection between representation collapse and the degradation of the trust region, one exacerbating the other. Finally, we present Proximal Feature Optimization (PFO), a novel auxiliary loss that, along with other interventions, shows that regularizing the representation dynamics mitigates the performance collapse of PPO agents.

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Citations (3)

Summary

  • The paper identifies that PPO suffers from representation collapse, where diminishing feature diversity undercuts the trust region mechanism and hampers performance.
  • It proposes Proximal Feature Optimization with a novel regularization term to preserve pre-activation norms and sustain robust policy training.
  • Empirical tests in Arcade Learning and MuJoCo environments demonstrate that PFO increases feature rank and agent performance while enhancing reproducibility.

Understanding Representation Dynamics in Proximal Policy Optimization

Introduction to the Paper's Core Challenges and Discoveries

In the world of reinforcement learning (RL), continuous adaptation to ever-changing environments is key to an agent's success. However, not all RL methods maintain robustness in dynamic scenarios—especially in the high variability seen in off-policy and on-policy training landscapes. The subject of this discussion, a paper on Proximal Policy Optimization (PPO), reveals the intrinsic challenges linked with representation degradation and proposes a novel solution to counteract this issue.

Core Problems with PPO Representation Dynamics

Representation Collapse

Traditionally, PPO has been lauded for its robust performance over extended training periods. However, researchers have identified a significant issue termed representation collapse where the model experiences a decrease in the diversity (rank) of its learned features. This degeneration is not immediately apparent because it happens beneath the seemingly stable performance surface. As training progresses, especially under strong non-stationarity, this collapse in feature variety leads to degraded performance and model plasticity.

Connection to Trust Region and Performance Collapse

Interestingly, the collapse of representation ties back to trust region problems in PPO. Originally, PPO implements a trust region to avoid drastic policy updates, theoretically avoiding significant policy performance drops. Unfortunately, when feature representation diminishes in richness, the clipping mechanism of PPO becomes ineffective. This ineffectiveness causes a direct impact on the agent’s ability to recover or adapt, leading to what is labeled as a performance collapse.

Major Contributions and Solutions

Proximal Feature Optimization (PFO)

In response to these challenges, the paper introduces Proximal Feature Optimization (PFO), which adds a regularization term to the loss function based on the change in pre-activations. This new component effectively mitigates the rate at which pre-activation norms increase, thus preserving feature rank and helping maintain a robust performance level throughout training.

Empirical Results

PFO was extensively tested in both Arcade Learning Environment and MuJoCo setups. It showed a consistent positive impact across several games, increasing both the feature rank and overall agent performance. By preventing the drastic explosion in feature representation (often seen in the pre-activation layers), PFO helps sustain a more diverse and effective feature set for decision-making.

Open Source Contribution

A commendable aspect of this research is the open-source commitment. The authors have provided the entire codebase and extensive run histories. This transparency not only reinforces the validity of the findings but also sets a stage for future research and verification by the wider AI and RL community.

Future Implications and Speculations

This paper sets the stage for a deeper investigation into various auxiliary losses and interventions that could further stabilize training in face of non-stationarity. The introduction of PFO also opens new avenues in how regularization tactics can be integrated within RL algorithms to tackle underlying representational shifts without compromising on the explorative capabilities needed in complex environments.

By refining these interventions, future research could lead to more resilient RL agents capable of tackling a wider array of tasks with higher initial unpredictability and complexity. Furthermore, this approach could enhance the understanding of how different features interact over prolonged training periods, potentially leading to even more innovative optimization strategies in on-policy learning regimes.

Conclusion

The dissection of PPO’s vulnerability to representation degradation not only heightens awareness about an often-overlooked issue but also provides a sophisticated method to counteract its impacts. This paper, through rigorous empirical examination and intuitive solution propositions, enriches the community's collective understanding and toolkit for dealing with the subtleties of policy optimization in changing environments.