Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Scalable Online Exploration via Coverability
Authors: Philip Amortila, Dylan J Foster, Akshay Krishnamurthy
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 66,573 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,397 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we find that L1-Coverage effectively drives off-the-shelf policy optimization algorithms to explore the state space. We present proof-of-concept experiments to validate our theoretical results. |
| Researcher Affiliation | Collaboration | 1University of Illinois, Urbana-Champaign. 2Microsoft Research. |
| Pseudocode | Yes | Algorithm 1 Approximate Policy Cover Computation via L -Coverability Relaxation. Algorithm 2 Coverage-Driven Exploration (CODEX). |
| Open Source Code | Yes | Code available at github.com/philip-amortila/l1-coverability. |
| Open Datasets | Yes | We focus on the planning problem (Section 4), and consider the classical Mountain Car environment (Brockman et al., 2016). |
| Dataset Splits | No | The paper describes the environment setup and data generation process (e.g., 'deterministic starting state,' 'discretization'), but it does not specify explicit train/validation/test dataset splits or percentages for a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'PyTorch (Paszke et al., 2019)', 'Adam optimizer (Kingma & Ba, 2015)', and 'Open AI Gym (Brockman et al., 2016)', but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We take a discount factor of 0.99, and a variance smoothing parameter of σ = 0.05. We train REINFORCE with horizons of length 400. We take πt, the policy which approximates Line 4 of Algorithm 1, to be the policy returned after 1000 REINFORCE updates, with one update after each rollout. The update in REINFORCE use the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 10 3. We estimate all occupancies with N = 100 rollouts of length H = 200. We train for 20 epochs, corresponding to T = 20 in the loop of Line 3 of Algorithm 1. For the regularized reward of Eq. (16), we take ε = 10 4. |