Benjamin Eysenbach

6 papers Princeton

• 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
• 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
• Contrastive Representations for Temporal Reasoning
• Horizon Reduction Makes RL Scalable
• Normalizing Flows are Capable Models for Continuous Control
• Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations