Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Authors: Yantian Zha, Lin Guan, Subbarao Kambhampati
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that an RLf D model can be improved by using our SERLf D framework in terms of training stability and performance. |
| Researcher Affiliation | Academia | Arizona State University {yantian.zha,guanlin,rao}@asu.edu |
| Pseudocode | Yes | Algorithm 1: The SERLf D Learning Algorithm |
| Open Source Code | Yes | To foster further research in self-explanation-guided robot learning, we have made our demonstrations and code publicly accessible at https://github.com/YantianZha/SERLfD. |
| Open Datasets | Yes | To foster further research in self-explanation-guided robot learning, we have made our demonstrations and code publicly accessible at https://github.com/YantianZha/SERLfD. |
| Dataset Splits | No | The paper does not explicitly mention training/validation/test splits or cross-validation methodology. |
| Hardware Specification | No | The paper describes the simulated robot and environment (Fetch Mobile Manipulator in PyBullet simulator) but does not provide specific details about the computing hardware (e.g., CPU, GPU, memory) used for experiments. |
| Software Dependencies | No | The paper mentions software components like 'PyBullet simulator', 'Twin-Delayed DDPG (TD3)', and 'Soft-Actor Critic (SAC)', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Each training episode had a maximum of 50 steps. |