6 papers
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal +3
Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are only able to produce near-optimal policie…
RLZero: Direct Policy Inference from Language Without In-Domain Supervision
Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo +6
The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward sign…
Clink! Chop! Thud! -- Learning Object Sounds from Real-World Interactions
Mengyu Yang, Yiming Chen, Haozheng Pei +3
Can a model distinguish between the sound of a spoon hitting a hardwood floor versus a carpeted one? Everyday object interactions produce sounds unique to the objects involved. We…
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
Caleb Chuck, Fan Feng, Carl Qi +4
Hindsight relabeling is a powerful tool for overcoming sparsity in goal-conditioned reinforcement learning (GCRL), especially in certain domains such as navigation and locomotion.…
Proto Successor Measure: Representing the Behavior Space of an RL Agent
Siddhant Agarwal, Harshit Sikchi, Peter Stone +1
Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment without additional interactions. Refe…
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer
Adam Labiosa, Zhihan Wang, Siddhant Agarwal +10
Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge. Model-free reinforcement learning (RL) i…