Instance-based Generalization in Reinforcement Learning
Authors: Martin Bertran, Natalia Martinez, Mariano Phielipp, Guillermo Sapiro
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate IRL on several standard continuous control environments, including Acrobot, CartPole, MountainCar, and LunarLander. Table 1 shows the performance of IRL on four continuous control environments. |
| Researcher Affiliation | Academia | The provided paper text does not contain any clear institutional affiliations, email domains, or author addresses to classify the affiliation type. |
| Pseudocode | Yes | Algorithm 1: Instance-based Reinforcement Learning |
| Open Source Code | Yes | The code for Instance-based Reinforcement Learning (IRL) is available at [https://github.com/IRL_project/code](https://github.com/IRL_project/code). |
| Open Datasets | Yes | We evaluate IRL on several standard continuous control environments, including Acrobot, CartPole, MountainCar, and LunarLander. |
| Dataset Splits | No | The paper mentions 'a standard 80/20 train-test split for data used to train the instance memory' but does not specify a separate validation dataset split. |
| Hardware Specification | Yes | All experiments were conducted on a machine equipped with an Intel Core i9-10900K CPU, 64GB RAM, and an NVIDIA GeForce RTX 3090 GPU. |
| Software Dependencies | Yes | Our implementation uses Python 3.8.5, PyTorch 1.10.0, and OpenAI Gym 0.21.0. |
| Experiment Setup | Yes | For all environments, we used a learning rate of 0.001, a batch size of 64, and a replay buffer size of 100,000. The discount factor was set to 0.99. We used the Adam optimizer. |