AdaPlanner: Adaptive Planning from Feedback with Language Models
Authors: Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments in the ALFWorld and Mini Wo B++ environments demonstrate that Ada Planner outperforms state-of-the-art baselines by 3.73% and 4.11% while utilizing 2x and 600x fewer samples, respectively. |
| Researcher Affiliation | Academia | Haotian Sun1 , Yuchen Zhuang1 , Lingkai Kong1, Bo Dai1, Chao Zhang1 1 Georgia Institute of Technology {haotian.sun, yczhuang, lkkong, chaozhang}@gatech.edu, bodai@cc.gatech.edu |
| Pseudocode | Yes | Figure 2: An illustrative example from ALFWorld to show the proposed adaptive closed-loop planning through code. The task is to put some clean lettuce on the diningtable... (a) Initial Plan (Extracted) def solution(agent, start_from=3):... |
| Open Source Code | Yes | The implementation of Ada Planner is available at https://github.com/haotiansun14/Ada Planner. |
| Open Datasets | Yes | We test Ada Planner on two text-based decision-making environments: 1) ALFWorld [20] is a text-based virtual household environment... 2) Mini Wo B++ [11] is a simulation environment... |
| Dataset Splits | No | The paper refers to |
| Hardware Specification | No | The paper mentions using specific LLM models (e.g., GPT-3.5, text-davinci-002) but does not provide details on the hardware (CPU, GPU, RAM) used to run the experiments or host these models. |
| Software Dependencies | Yes | RCI and Ada Planner harness GPT-3.5 (gpt-3.5-turbo and text-davinci-003) as backends. |
| Experiment Setup | Yes | The Setup details and prompts for Ada Planner are depicted in Appendix A and D. |