Reinforcement Learning
Creating Multi-Level Skill Hierarchies in Reinforcement Learning
What is a useful skill hierarchy for an autonomous agent?
Imitation Learning from Vague Feedback
Imitation learning from human feedback studies how to train well-performed imitation agents with an annotator's relative comparison of two demonstrations
Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees รor de ลฝikeli c
Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment.
A Robust and Opponent-Aware League Training Method for StarCraft II
In this paper, we improve AlphaStar's league training
Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds
Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms.
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Easily integrated, FamO2O statistically enhances existing algorithms' performance.
ELF-R
This mostly includes tasks with multifaceted objectives, such as dialogue response generation, or tasks with hard-to-define goals, such as enhancing program readability.
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In Reinforcement Learning (RL), an agent engages with an environment in a sequential manner.