Reinforcement Learning
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation Long-Fei Li
Reinforcement Learning (RL) with function approximation has achieved remarkable success in various applications involving large state and action spaces, such as games [Silver et al., 2016],
Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus
Additionally, methods that utilize the bisimulation metric for evaluating state discrepancies face a theory-practice gap due to improper approximations in metric learning, particularly struggling with hard exploration tasks.
GT A: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning
Offline Reinforcement Learning (Offline RL) presents challenges of learning effective decision-making policies from static datasets without any online interactions.
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
The interaction is usually modeled as Markov Decision Processes (MDPs). Research on MDPs can be broadly divided into two lines based on the reward generation mechanism. The first line of work [Jaksch et al., 2010, Azar et al., 2013, 2017, He et al., 2021] considers the
Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
We adopt a multi-armed bandit framework for this process, enhancing prompt selection based on the returns from online trajectories.
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agents in various real-world applications. Previous Cross-Embodiment RL approaches have focused on transferring knowledge across embodiments within specific tasks.
