Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning (2105.08140v1)

Abstract: Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. However, existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We hypothesize that a key missing ingredient from the existing methods is a proper treatment of uncertainty in the offline setting. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs and down-weights their contribution in the training objectives accordingly. Implementation-wise, we adopt a practical and effective dropout-based uncertainty estimation method that introduces very little overhead over existing RL algorithms. Empirically, we observe that UWAC substantially improves model stability during training. In addition, UWAC out-performs existing offline RL methods on a variety of competitive tasks, and achieves significant performance gains over the state-of-the-art baseline on datasets with sparse demonstrations collected from human experts.

• The paper proposes UWAC, which dynamically weights state-action pairs using dropout-based uncertainty estimates to mitigate errors from OOD inputs.
• It demonstrates improved training stability and superior performance over methods like BEAR and CQL on MuJoCo and other benchmark tasks.
• The paper offers a scalable framework that opens avenues for integrating uncertainty weighting with model-based and transfer RL approaches.

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

The research paper titled "Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning" explores the challenges associated with offline reinforcement learning (RL) when dealing with out-of-distribution (OOD) state-action pairs. The paper introduces a novel method named Uncertainty Weighted Actor-Critic (UWAC), which enhances the stability and performance of offline RL algorithms by incorporating uncertainty estimations into the learning process.

Key Contributions

1. Problem Context: Offline RL has gained traction due to its potential to learn policies from static datasets without the need for active exploration. However, existing algorithms, particularly those based on Q-learning and actor-critic methods, often struggle when making predictions on OOD state-action pairs, leading to instabilities during training. This paper posits that a significant gap in current methodologies is the inadequate handling of uncertainty in these scenarios.
2. Algorithmic Framework - UWAC: The central innovation of this work is UWAC, which identifies OOD state-action pairs and adjusts their influence on the training objectives based on the estimated uncertainty. The algorithm employs a dropout-based uncertainty estimation method, offering a computationally efficient approach with minimal additional overhead compared to standard RL algorithms.
3. Empirical Insights: The empirical evaluation of UWAC demonstrates improvements in training stability and performance over conventional offline RL techniques. Notably, UWAC outperforms existing methods across a variety of challenging tasks and exhibits marked performance gains on datasets derived from sparse demonstrations by human experts.
4. Theoretical Implications: By integrating uncertainty estimation into the policy and value functions, UWAC provides a mechanism to stabilize Q-value estimates during training. This approach mitigates the propagation of errors resulting from OOD state-action pairs, thus fostering better convergence properties.
5. Performance Evaluation: The application of UWAC on benchmark datasets such as the MuJoCo walkers and the Adroit hand manipulation tasks reveals its superiority in handling datasets with limited state-action coverage. The significant improvements in performance metrics underscore the efficacy of the proposed method in achieving robust offline RL.

Numerical Results and Analysis

The paper presents strong numerical results, indicating that UWAC sets new benchmarks across various offline RL tasks. For example, it outperforms BEAR and CQL on the D4RL MuJoCo datasets. The experiment results confirm that UWAC significantly reduces the bootstrapping errors that plague traditional methods by leveraging uncertainty estimates to modulate the training loss.

Future Directions

This research opens several promising avenues for future exploration within the field of RL. One potential direction is the integration of UWAC with model-based RL approaches to further enhance the robustness of policy learning from limited data. Additionally, further investigations could explore the application of uncertainty-weighting principles in other RL paradigms, such as online learning and transfer learning scenarios.

In conclusion, UWAC represents an important step forward in the development of stable and efficient offline RL algorithms. By effectively addressing the challenge of OOD state-action pairs through the lens of uncertainty estimation, this work not only extends the capabilities of RL systems to learn from static datasets but also sets a foundation for future advancements in the area of RL under uncertainty.