Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments (2504.19139v3)

Abstract: Task robust adaptation is a long-standing pursuit in sequential decision-making. Some risk-averse strategies, e.g., the conditional value-at-risk principle, are incorporated in domain randomization or meta reinforcement learning to prioritize difficult tasks in optimization, which demand costly intensive evaluations. The efficiency issue prompts the development of robust active task sampling to train adaptive policies, where risk-predictive models are used to surrogate policy evaluation. This work characterizes the optimization pipeline of robust active task sampling as a Markov decision process, posits theoretical and practical insights, and constitutes robustness concepts in risk-averse scenarios. Importantly, we propose an easy-to-implement method, referred to as Posterior and Diversity Synergized Task Sampling (PDTS), to accommodate fast and robust sequential decision-making. Extensive experiments show that PDTS unlocks the potential of robust active task sampling, significantly improves the zero-shot and few-shot adaptation robustness in challenging tasks, and even accelerates the learning process under certain scenarios. Our project website is at https://thu-rllab.github.io/PDTS_project_page.

Overview of Posterior and Diversity Synergized Task Sampling in Adaptive Decision-Makers

The paper "Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments," presents an innovative method known as Posterior and Diversity Synergized Task Sampling (PDTS) for improving the robustness and efficiency of adaptive decision-making in randomized environments. The method addresses the challenge of transferring reinforcement learning (RL) policies to unseen scenarios effectively, without extensive retraining. The proposed framework is grounded in the notion of robust active task sampling within the risk-sensitive domain randomization (DR) and meta reinforcement learning (Meta-RL) paradigms.

Main Contributions

The paper's primary contributions are multi-faceted:

1. Introduction of i-MAB Framework: The authors abstract the robust active task sampling as a task-selection Markov Decision Process (MDP), utilizing an infinitely many-armed bandit (i-MAB) approach for theoretically modeling the adaptive decision-making process.
2. Enhancement with Diversity Regularization: It proposes a diversity regularized acquisition strategy that effectively counters the subset concentration issue seen in large state spaces, such as those governed by high risk predictive models like MPTS. This regularization enhances exploration over wide-ranging task sets and secures nearly worst-case MDP robustness.
3. Posterior Sampling Application: The paper shifts the acquisition function from the upper confidence bound (UCB) approach to posterior sampling. This strategic change reduces computational overhead while maintaining uncertainty optimism during decision-making.
4. Demonstrated Empirical Robustness: The experimental findings claim PDTS surpasses current state-of-the-art baselines in adaptation robustness across typical benchmarks in DR and Meta-RL. Notably, PDTS maintains marked performance improvements in both average returns and robustness in vision-based, realistic scenarios.

Numerical Results and Discussion

Extensive experiments showcase PDTS's superiority in zero-shot and few-shot adaptation settings compared to established methods like Expected Risk Minimization (ERM), Distributionally Robust Risk Minimization (DRM), and MPTS. Particularly, PDTS consistently achieves higher cumulative return values in environments like Walker2d and HalfCheetah for various CVaR thresholds.

Significantly, PDTS's adoption of a larger candidate task batch (up to 64 times the regular size in certain experiments) has been instrumental in avoiding the performance collapse associated with traditional methods when increasing pseudo batch sizes. This improvement is attributed to the enhanced exploration capacity yielded by its diversity regularization and posterior sampling.

Practical and Theoretical Implications

Theoretically, this work contributes to the understanding of task-robust optimization in sequential decision-making by framing active task sampling as an MDP problem. By leveraging i-MABs, the authors posit a novel optimization framework that potentially benefits future algorithmic developments aimed at tackling large-scale decision-making problems with efficiency constraints.

Practically, PDTS's fast and robust adaptation mechanism is poised to significantly impact real-world applications, including autonomous driving and robotics, where adaptation failures could result in catastrophic consequences. Though human supervision remains necessary, the robustness attributed to PDTS might minimize adaptation errors.

Speculations on Future AI Developments

While current methods like PDTS show marked improvements in individualized task sampling under randomized environments, future research could explore refining risk predictive models for greater accuracy and efficiency. The integration of advanced learning techniques to further automate and scale task sampling processes presents a promising avenue for exploration. Furthermore, PDTS's success may catalyze broader applications across domains where adapting models to new environments rapidly and reliably is critical.

In conclusion, PDTS represents a pivotal advancement in the robust active task sampling domain, offering a practical solution to improve the deployment versatility of RL policies. This could foreseeably accelerate the development and deployment of adaptive AI systems in complex and dynamic real-world settings.