Prioritized Level Replay (2010.03934v4)

Abstract: Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset of levels allows for testing generalization to unseen levels. What can be learned from a level depends on the current policy, yet prior work defaults to uniform sampling of training levels independently of the policy. We introduce Prioritized Level Replay (PLR), a general framework for selectively sampling the next training level by prioritizing those with higher estimated learning potential when revisited in the future. We show TD-errors effectively estimate a level's future learning potential and, when used to guide the sampling procedure, induce an emergent curriculum of increasingly difficult levels. By adapting the sampling of training levels, PLR significantly improves sample efficiency and generalization on Procgen Benchmark--matching the previous state-of-the-art in test return--and readily combines with other methods. Combined with the previous leading method, PLR raises the state-of-the-art to over 76% improvement in test return relative to standard RL baselines.

• The paper introduces Prioritized Level Replay, which reorders training levels using scores derived from TD errors and staleness measures.
• It integrates seamlessly with any RL algorithm, demonstrating a 76% improvement in mean test returns on the Procgen Benchmark with image augmentation.
• The approach effectively curates an emergent curriculum that enhances sample efficiency and mitigates overfitting in varied procedural environments.

An Expert Overview of "Prioritized Level Replay"

The paper entitled "Prioritized Level Replay (PLR)" investigates a novel approach to enhance the generalization capacity of deep reinforcement learning (RL) agents in procedurally generated (PCG) environments. The authors present a sampling methodology that adaptively prioritizes training levels based on their projected learning potential, a departure from the prevalent uniform sampling strategy. By reordering experiences to form an emergent curriculum, PLR targets the intrinsic issue of overfitting that afflicts RL models when exposed to fixed training regimes.

Core Contributions and Methodology

The core contribution of this research lies in its innovative mechanism of "Prioritized Level Replay." This method dynamically prioritizes the selection of training levels to optimize the agent's learning trajectory. Two distribution systems underpin PLR—one based on learning potential estimated via temporal-difference (TD) errors and another based on the staleness of previously sampled levels. This dual approach ensures a balance between leveraging the latest meaningful experiences and maintaining fresh interactions with the environment.

1. Level Scoring and Sampling: The scoring metric leveraged in PLR is derived from the absolute Generalized Advantage Estimate (GAE), which is reflective of the average value discrepancy encountered by the agent. This score is indicative of each level's capacity to offer further learning, thus steering the sampling towards experiences likely to yield maximum policy improvements.
2. Staleness-Aware Component: PLR employs a mixture model combining level scores with staleness priorities to maintain a distribution that ensures all relevant levels are explored in a timely manner, preventing reliance on outdated data and possible overfitting.
3. Algorithm Integration and Flexibility: Compatible with any RL algorithm, the PLR framework proposes a flexible drop-in replacement for experience collection steps in training loops. Notably, it does not rely on specific architecture adjustments or advanced hyperparameters, integrating seamlessly into existing training models.

Experimental Results and Implications

The empirical results underscore PLR's efficacy in amplifying the sample efficiency and generalization across a suite of environments from Procgen Benchmark to challenging MiniGrid domains. On Procgen Benchmark, PLR sets a new benchmark when combined with image augmentation techniques such as UCB-DrAC, achieving a 76% relative improvement in mean test returns over standard RL baselines. Additionally, the results in MiniGrid environments further corroborate the hypothesis that PLR facilitates continuous progression through a curriculum of challenges adjusted to the agent's current capabilities.

Future Perspectives and Theoretical Implications

Examining the broader theoretical implications, the adoption of PLR in varied environments hints at its potential to influence areas extending beyond PCG environments, notably in real-world scenarios where singletons are inflexible and environmental resets are impractical. The clear adaptive and versatile nature of PLR suggests its applicability in sim-to-real transfer scenarios, where simulation-based pre-training relies extensively on procedural variations for comprehensive policy learning.

Furthermore, this approach opens avenues for further exploration in goal-conditioned settings and environments demanding more explicit curriculum learning. Examining how PLR might complement existing exploration strategies could yield additional insights into optimizing RL training paradigms.

Conclusion

The "Prioritized Level Replay" framework presents a significant step toward overcoming generalization challenges inherent in procedural content-driven RL by efficiently curating the learning experience into a self-optimizing curriculum. This paper outlines a solid foundation for future investigations that could refine curriculum learning strategies and drive more robust RL deployments in complex domains.