Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Self-Paced Deep Reinforcement Learning
Authors: Pascal Klink, Carlo D'Eramo, Jan R. Peters, Joni Pajarinen
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the conducted experiments, the curricula generated with the proposed algorithm significantly improve learning performance across several environments and deep RL algorithms, matching or outperforming state-of-the-art existing CRL algorithms. |
| Researcher Affiliation | Academia | Pascal Klink1, Carlo D Eramo1, Jan Peters1, Joni Pajarinen1,2 1 Intelligent Autonomous Systems, Technische Universität Darmstadt, Germany 2 Department of Electrical Engineering and Automation, Aalto University, Finland |
| Pseudocode | Yes | Algorithm 1 Self-Paced Deep Reinforcement Learning |
| Open Source Code | Yes | Code for running the experiments can be found at https://github.com/psclklnk/spdl |
| Open Datasets | Yes | We use the Open AI Gym simulation environment [53]... We use the Nvidia Isaac Gym simulator [54] for this experiment. |
| Dataset Splits | No | The paper describes training and evaluation in continuous environments but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions 'on our hardware' but does not provide specific details such as GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions software like 'Stable Baselines library' and 'SciPy library' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We evaluate the performance using TRPO [16], PPO [17] and SAC [18]. For all DRL algorithms, we use the implementations provided in the Stable Baselines library [52]. ... In each iteration, the parameter αi is chosen such that the KL divergence penalty w.r.t. the current context distribution is in constant proportion ζ to the average reward obtained during the last iteration of policy optimization αi = B(νi, Di) = ζ (1/K PK k=1 R τ k, ck / DKL (pνi(c) µ(c))) ... For the experiments, we restrict pν(c) to be Gaussian. |