Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
cs.AI, cs.LG, cs.RO
Submitted: 2026-03-16
Updated: 2026-08-25
Terminology
Sources
- Variational Option Discovery Algorithms
- Proto Successor Measure: Representing the Behavior Space of an RL Agent
- Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?
- Successor Features for Transfer in Reinforcement Learning
- Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
- OpenAI Gym
- Exploration by Random Network Distillation
- Diversity is All You Need: Learning Skills without a Reward Function
- The Information Geometry of Unsupervised Reinforcement Learning
- Diagnosing Bottlenecks in Deep Q-learning Algorithms
- D4RL: Datasets for Deep Data-Driven Reinforcement Learning
- A Minimalist Approach to Offline Reinforcement Learning
- Off-Policy Deep Reinforcement Learning without Exploration
- Towards General-Purpose Model-Free Reinforcement Learning
- Ego4D: Around the World in 3,000 Hours of Egocentric Video
- Bootstrap your own latent: A new approach to self-supervised Learning
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- Dream to Control: Learning Behaviors by Latent Imagination
- Temporal Difference Learning for Model Predictive Control
- Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization
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