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Found 7 papers, 1 papers with code
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective.
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression
Recent advancements in reinforcement learning (RL) have led to remarkable achievements in robot locomotion capabilities.
Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System
We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot motions.
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Discrete optimization belongs to the set of $\mathcal{NP}$-hard problems, spanning fields such as mixed-integer programming and combinatorial optimization.
RObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks
In this work, we present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN), to address the challenge of solving multiple complex tasks over long time horizons in robotic manipulation.
Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory
To address this, we propose a new approach to apply verification methods from control theory to learned value functions.
Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations
To proactively navigate and traverse various terrains, active use of visual perception becomes indispensable.
