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Found 6 papers, 1 papers with code
The Third Monocular Depth Estimation Challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
Scale-Invariant Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
This novel transformation process ensures that the converted constraints are invariant to the objective scales while having the same effect as the original objectives.
Multi-Objective Reinforcement Learning
reinforcement-learning
Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk
This paper aims to solve a safe reinforcement learning (RL) problem with risk measure-based constraints.
TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning
In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC.
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints must be met, such as avoiding collisions, limiting energy consumption, and maintaining balance.
Distributional Reinforcement Learning
reinforcement-learning
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KOBEST: Korean Balanced Evaluation of Significant Tasks
To this end, we propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks.
