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.
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
Authors: Vint Lee, Pieter Abbeel, Youngwoon Lee
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 18,998 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,417 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 | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2Yonsei University |
| Pseudocode | Yes | Algorithm 1 COLLECT ROLLOUT (π: policy, D: replay buffer) in DREAMSMOOTH |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that the source code for their methodology is available. |
| Open Datasets | Yes | We evaluate Dream Smooth on four tasks with sparse subtask completion rewards and two common RL benchmarks. Earthmoving uses two 64 × 64 images as an observation while all other tasks use a single image. See Appendix C for environment details. Robo Desk: We use a modified version of Robo Desk (Kannan et al., 2021)... Hand: The Hand task (Plappert et al., 2018)... Earthmoving: The agent controls a wheel loader... Crafter: Crafter (Hafner, 2022)... DMC: We benchmark 7 Deep Mind Control Suite continuous control tasks (Tassa et al., 2018). Atari: We benchmark 6 Atari tasks (Bellemare et al., 2013) at 100K steps. |
| Dataset Splits | No | The paper refers to 'evaluation episodes' and describes training and testing processes, but it does not explicitly specify data splits for training, validation, and testing sets, nor does it mention cross-validation techniques with concrete details. |
| Hardware Specification | Yes | Models are trained on NVIDIA A5000, V100, RTX Titan, RTX 2080, and RTX 6000 GPUs. |
| Software Dependencies | No | The paper mentions using 'scipy.ndimage' functions but does not specify version numbers for any software dependencies, such as Python, PyTorch, or Scipy itself. |
| Experiment Setup | Yes | Hyperparameters for Dreamer V3, TD-MPC, and MBPO experiments are shown in Table 1, Table 2, and Table 3, respectively. |