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Found 6 papers, 2 papers with code
Making RL with Preference-based Feedback Efficient via Randomization
Reinforcement Learning algorithms that learn from human feedback (RLHF) need to be efficient in terms of statistical complexity, computational complexity, and query complexity.
Contextual Bandits and Imitation Learning via Preference-Based Active Queries
We consider the problem of contextual bandits and imitation learning, where the learner lacks direct knowledge of the executed action's reward.
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
In online RL, we propose a DistRL algorithm that constructs confidence sets using maximum likelihood estimation.
Distributional Offline Policy Evaluation with Predictive Error Guarantees
Our theoretical results show that for both finite-horizon and infinite-horizon discounted settings, FLE can learn distributions that are close to the ground truth under total variation distance and Wasserstein distance, respectively.
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
In constrained multi-objective RL, the goal is to learn a policy that achieves the best performance specified by a multi-objective preference function under a constraint.
Multi-Objective Reinforcement Learning
reinforcement-learning
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.
