Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL
cs.LG
Submitted: 2026-08-31
Updated: 2026-08-31
Code: https://github.com/M-Korniak/action-chunked-contrastive-rl
Terminology
Sources
- TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning
- Learning to Understand Goal Specifications by Modelling Reward
- Distributed Distributional Deterministic Policy Gradients
- Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
- Training-Time Action Conditioning for Efficient Real-Time Chunking
- Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- Metrics for Finite Markov Decision Processes
- Grounded Language Learning in a Simulated 3D World
- Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning
- Decoupled Q-Chunking
- Reinforcement Learning with Action Chunking
- Flow Matching for Generative Modeling
- Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
- Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
- Representation Learning with Contrastive Predictive Coding
- Foundation Policies with Hilbert Representations
- Adaptive Action Chunking via Multi-Chunk Q Value Estimation
- State Representation Learning for Goal-Conditioned Reinforcement Learning
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