Advancements in Tensor-Based Learning and Optimization for Reinforcement Learning and Bandit Algorithms

The recent developments in the research area of reinforcement learning and bandit algorithms have shown a significant shift towards addressing high-dimensional and complex decision-making problems through innovative mathematical frameworks and optimization techniques. A notable trend is the exploration of low-rank tensor structures and entropy-regularized objectives to enhance the efficiency and scalability of learning algorithms. These approaches not only facilitate the handling of multi-task and finite-horizon Markov Decision Processes (MDPs) but also extend to generalized tensor bandits and preconditioner learning, showcasing the versatility of tensor-based methods in various applications. Additionally, the integration of reinforcement learning with algorithmic and hardware optimizations for embedded systems highlights the growing importance of real-time, autonomous decision-making capabilities in resource-constrained environments. The advancements in modeling attention mechanisms and optimizing return distributions further underscore the field's progression towards more nuanced and sophisticated learning paradigms.

Noteworthy Papers

  • EigenVector-based Average-reward Learning: Introduces a neural network-based approach for entropy-regularized average-reward RL, revealing new theoretical insights and demonstrating superior stability and convergence rates.
  • A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning: Proposes a low-rank tensor approach for multi-task learning, effectively reducing data acquisition needs and demonstrating efficiency in practical scenarios.
  • Solving Finite-Horizon MDPs via Low-Rank Tensors: Develops a scalable low-rank tensor framework for finite-horizon MDPs, significantly reducing computational demands.
  • PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning: Presents a novel RL approach for learning matrix preconditioners, offering improved flexibility and solving speed.
  • Optimizing Return Distributions with Distributional Dynamic Programming: Extends distributional DP methods to optimize a broader class of return distribution objectives, with practical applications in risk-sensitive RL.

Sources

EVAL: EigenVector-based Average-reward Learning

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning

Solving Finite-Horizon MDPs via Low-Rank Tensors

A Unified Regularization Approach to High-Dimensional Generalized Tensor Bandits

PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning

Modeling Attention during Dimensional Shifts with Counterfactual and Delayed Feedback

Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints

Optimizing Return Distributions with Distributional Dynamic Programming

Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems

Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent

Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits

Built with on top of