Reinforcement Learning
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.
A Reduction-based Framework for Sequential Decision Making with Delayed Feedback Y unchang Y ang 1 Han Zhong 1 Tianhao Wu2 Bin Liu 3
More examples include but are not limited to robotics (Mahmood et al.,
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
Unlike most reinforcement learning agents which require an unrealistic amount of environment interactions to learn a new behaviour, humans excel at learning quickly by merely observing and imitating others.
Automatic Grouping for Efficient Cooperative Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (GoMARL), which learns automatic grouping without domain knowledge for efficient cooperation.
Active Observing in Continuous-time Control
The decision-maker's observing policy must be timely