Uncertainty Prioritized Experience Replay (2506.09270v1)

Abstract: Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based deep reinforcement learning models. Typically, transitions are prioritized based on their temporal difference error. However, this approach is prone to favoring noisy transitions, even when the value estimation closely approximates the target mean. This phenomenon resembles the noisy TV problem postulated in the exploration literature, in which exploration-guided agents get stuck by mistaking noise for novelty. To mitigate the disruptive effects of noise in value estimation, we propose using epistemic uncertainty estimation to guide the prioritization of transitions from the replay buffer. Epistemic uncertainty quantifies the uncertainty that can be reduced by learning, hence reducing transitions sampled from the buffer generated by unpredictable random processes. We first illustrate the benefits of epistemic uncertainty prioritized replay in two tabular toy models: a simple multi-arm bandit task, and a noisy gridworld. Subsequently, we evaluate our prioritization scheme on the Atari suite, outperforming quantile regression deep Q-learning benchmarks; thus forging a path for the use of uncertainty prioritized replay in reinforcement learning agents.

• The paper presents a novel decomposition of total uncertainty into epistemic and aleatoric components for better experience prioritization.
• It integrates ensemble predictions and quantile errors to quantify target epistemic uncertainty and derive an information gain metric.
• Empirical validations in tabular and Atari environments show improved convergence and performance over traditional PER methods.

Uncertainty Prioritized Experience Replay: An Advanced Approach in Reinforcement Learning

In this paper, the authors explore a novel approach to improve reinforcement learning (RL) sample efficiency by introducing Uncertainty Prioritized Experience Replay (UPER). The technique fundamentally enhances the selection of transitions from a replay buffer by incorporating epistemic uncertainty estimates in decision-making. Traditional Prioritized Experience Replay (PER) methods prioritize transitions using temporal difference (TD) errors. However, they typically neglect the aleatoric uncertainty inherent in noisy environments, which can lead to suboptimal learning paths. The proposed UPER methodology targets this limitation by effectively estimating and balancing both epistemic and aleatoric uncertainty through a quantifiable metric known as information gain.

Methodological Advancements

A primary contribution of the paper is the decomposition of total uncertainty into target epistemic uncertainty and aleatoric uncertainty, extending upon existing frameworks. This decomposition is achieved by assessing average squared errors relative to the target across quantiles and ensemble predictions. By considering both epistemic uncertainty (estimable and reducible through learning) and aleatoric uncertainty (inherent noise and irreducible), UPER prioritizes transitions based on how informative they are likely to be, maximizing learning efficiency.

Key elements of the decomposition are articulated through novel formulations, such as:

1. Target Epistemic Uncertainty: Integrating the squared estimation discrepancies from target values with ensemble disagreements, offering a robust measure against biases in model prediction.
2. Information Gain Criterion: Deriving a useful prioritization metric from Bayesian statistics, defined as the entropy reduction from obtaining new data. This incorporates both uncertainty types to align prioritization more closely with expected learning outcomes.

Empirical Validation

The efficacy of UPER is demonstrated across several settings. Initially, tabular environments like multi-armed bandit tasks and noisy gridworld illustrate UPER’s ability to favor transitions offering substantive learning opportunities, avoiding the pitfall of noisy but uninformative data inherent in classical PER methodologies. These experiments show that UPER prioritization leads better convergence rates and final performance compared to alternatives based purely on TD-errors.

Further application is explored in the Atari-57 suite, where UPER demonstrates notable improvements over standard QR-DQN and PER variations, thus substantiating the approach's wide applicability across complex RL domains. The ensemble of distributional RL agents, enriched with UPER priorities, consistently outperform baseline models, indicative of the method’s enhanced adaptability and robustness.

Implications and Future Directions

The findings suggest that UPER can significantly elevate RL model efficiency by deploying uncertainty measures in sample prioritization, avoiding common pitfalls associated with noise-heavy data. Theoretically, this aligns well with epistemic uncertainty handling in learning tasks beyond RL, such as supervised learning or active learning, rooting for potential crossover applications.

Future work could explore expanding UPER integration with other RL architectures beyond QR-DQN, as signified by promising initial results with C51 models. Additionally, alternative ways to estimate and combine aleatoric and epistemic uncertainty could further refine the prioritization criteria employed by UPER, pushing the boundaries in reinforcement learning and possibly beyond, into broader AI problem spaces.

Overall, UPER stands out as a significant progression in the efficient handling of experience replay, setting a precedent for accounting for uncertainties within RL to enhance generalization, learning speed, and policy effectiveness.