Reinforcement Learning
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
Additionally, it achieves good performance on designated MineDojo tasks.
A theoretical case-study of Scalable Oversight in Hierarchical Reinforcement Learning
To this end, we study the challenges of scalable oversight in the context of goal-conditioned hierarchical reinforcement learning.
Inverse Factorized Soft Q-Learning for Cooperative Multi-agent Imitation Learning
In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
Reinforcement learning utilizing kernel ridge regression to predict the expected value function represents a powerful method with great representational capacity. This setting is a highly versatile framework amenable to analytical results. We consider kernel-based function approximation for RL in the infinite horizon average reward setting, also referred to as the undiscounted setting.