awesome-gcrl

Research Papers

ICLR 2024

1. Zheng C, Eysenbach B, Walke H R, et al. Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data[C]//The Twelfth International Conference on Learning Representations. [Paper]

ICML 2024

1. Zheng S, Bai C, Yang Z, et al. How Does Goal Relabeling Improve Sample Efficiency?[C]//Forty-first International Conference on Machine Learning. 2024. [Paper]

2. Wang V H, Wang T, Yang W, et al. Probabilistic subgoal representations for hierarchical reinforcement learning[C]//Forty-first International Conference on Machine Learning. 2024. [Paper]

3. Jain V, Ravanbakhsh S. Learning to Reach Goals via Diffusion[C]//Forty-first International Conference on Machine Learning. 2024. [Paper]

4. Na H, Moon I. LAGMA: LAtent Goal-guided Multi-Agent Reinforcement Learning[C]//Forty-first International Conference on Machine Learning. 2024. [Paper]

5. Myers V, Zheng C, Dragan A, et al. Learning temporal distances: contrastive successor features can provide a metric structure for decision-making[C]//Forty-first International Conference on Machine Learning. 2024. 37076-37096. [Paper]

6. Xudong G, Dawei F, Xu K, et al. "Iterative Regularized Policy Optimization with Imperfect Demonstrations." Forty-first International Conference on Machine Learning. 2024. [Paper] [Code]

NeurIPS 2024

1. Gong X, Feng D, Xu K, et al. Goal-Conditioned On-Policy Reinforcement Learning[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024. [Paper] [Code]

2. Duan Y, Cui G, Zhu H. Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024. [Paper]

3. Cheng H, Brown J W. Goal Reduction with Loop-Removal Accelerates RL and Models Human Brain Activity in Goal-Directed Learning[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024. [Paper]

4. Yalcinkaya B, Lauffer N, Vazquez-Chanlatte M, et al. Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024. [Paper]

5. Wu J, Wang Y, Li L, et al. Goal Conditioned Reinforcement Learning for Photo Finishing Tuning[C]//The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024. [Paper]

ICLR 2025

1. Gong X, Feng D, Xu K, et al. VVC-Gym: A Fixed-Wing UAV Reinforcement Learning Environment for Multi-Goal Long-Horizon Problems[C]//The Thirteenth International Conference on Learning Representations. [Paper] [Code]

2. Park S, Frans K, Eysenbach B, et al. Ogbench: Benchmarking offline goal-conditioned rl[C]//The Thirteenth International Conference on Learning Representations. [Paper] [Code]

3. Bortkiewicz M, Pałucki W, Myers V, et al. Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research[C]//The Thirteenth International Conference on Learning Representations. [Paper] [Code]

4. Chuck C, Feng F, Qi C, et al. Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning[C]//The Thirteenth International Conference on Learning Representations. [Paper]

5. Luo Y, Du Y. Grounding Video Models to Actions through Goal Conditioned Exploration[C]//The Thirteenth International Conference on Learning Representations. [Paper]

6. Myers V, Ji C, Eysenbach B. Horizon Generalization in Reinforcement Learning[C]//The Thirteenth International Conference on Learning Representations. [Paper]

ICML 2025

1. Gong X, Yang S, Feng D, et al. Improving the Continuity of Goal-Achievement Ability via Policy Self-Regularization for Goal-Conditioned Reinforcement Learning[C]//Forty-second International Conference on Machine Learning. ICML, 2025. [Paper] [Code]

2. Ke K, Lin Q, Liu Z, et al. Conservative Offline Goal-Conditioned Implicit V-Learning[C]//Forty-second International Conference on Machine Learning. [Paper]

3. He J, Li K, Zang Y, et al. Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning[C]//Forty-second International Conference on Machine Learning. [Paper]

4. Richens J, Everitt T, Abel D. General agents need world models[C]//Forty-second International Conference on Machine Learning. [Paper]

GCRL environments

1. Gong X, Feng D, Xu K, et al. VVC-Gym: A Fixed-Wing UAV Reinforcement Learning Environment for Multi-Goal Long-Horizon Problems[C]//International Conference on Learning Representations. ICLR, 2025. [Paper] [Code]

2. Gymnasium-Robotics. [Code]

3. Gallouédec Q, Cazin N, Dellandréa E, et al. panda-gym: Open-source goal-conditioned environments for robotic learning[C]//4th Robot Learning Workshop: Self-Supervised and Lifelong Learning@ NeurIPS 2021. 2021. [Paper] [Code]

4. Park S, Frans K, Eysenbach B, et al. Ogbench: Benchmarking offline goal-conditioned rl[C]//International Conference on Learning Representations. ICLR, 2025. [Paper] [Code]

5. Bortkiewicz M, Pałucki W, Myers V, et al. Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research[C]//The Thirteenth International Conference on Learning Representations. [Paper] [Code]

6. Plappert M, Andrychowicz M, Ray A, et al. Multi-goal reinforcement learning: Challenging robotics environments and request for research[J]. arxiv preprint arxiv:1802.09464, 2018. [Paper]