Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Goal-Conditioned Predictive Coding for Offline Reinforcement Learning
Authors: Zilai Zeng, Ce Zhang, Shijie Wang, Chen Sun
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive empirical evaluations on Ant Maze, Franka Kitchen and Locomotion environments, we observe that sequence modeling can have a significant impact on challenging decision making tasks. |
| Researcher Affiliation | Academia | Zilai Zeng Brown University Ce Zhang Brown University Shijie Wang Brown University Chen Sun Brown University |
| Pseudocode | Yes | Appendix B Pseudocode of GCPC. Algorithm 1 Goal-conditioned Predictive Coding (GCPC) for Rv S |
| Open Source Code | Yes | Our code is available at https://brown-palm.github.io/GCPC/. |
| Open Datasets | Yes | To answer the questions above, we conduct extensive experiments on three domains from D4RL offline benchmark suite [16]: Ant Maze, Franka Kitchen and Gym Locomotion. |
| Dataset Splits | Yes | During policy learning, we use the pre-trained Traj Net that achieves the lowest validation reconstruction loss to generate the bottleneck. |
| Hardware Specification | Yes | All experiments are performed on a single Nvidia RTX A5000. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | Appendix A.3 Hyperparameter. We list hyperparameters for BC and Rv S-G/R replication in Table A1 and GCPC implementation in Table A2. For Rv S-G/R and Policy Net in GCPC, we use a two-layer feedforward MLP as the policy network, taking the current state and goal (state or return-to-go) as input, the only difference is that Policy Net takes the bottleneck as an additional input. All experiments are performed on a single Nvidia RTX A5000. |