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..
CAQL: Continuous Action Q-Learning
Authors: Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare CAQL with state-of-the-art RL algorithms on benchmark continuous-control problems that have different degrees of action constraints and show that CAQL outperforms policy-based methods in heavily constrained environments, often dramatically. ... We evaluate CAQL on one classical control benchmark (Pendulum) and five Mu Jo Co benchmarks (Hopper, Walker2D, Half Cheetah, Ant, Humanoid). |
| Researcher Affiliation | Industry | Moonkyung Ryu*, Yinlam Chow*, Ross Anderson, Christian Tjandraatmadja, Craig Boutilier Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Continuous Action Q-learning (CAQL) |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the open-sourcing of the CAQL implementation code developed in this paper. |
| Open Datasets | Yes | We evaluate CAQL on one classical control benchmark (Pendulum) and five Mu Jo Co benchmarks (Hopper, Walker2D, Half Cheetah, Ant, Humanoid). ... Table 6: Benchmark Environments. |
| Dataset Splits | No | The paper discusses training on data collected from simulation environments and stored in a replay buffer, sampling mini-batches for training. It does not specify fixed train/validation/test dataset splits in the conventional sense for static datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU/CPU models or cloud instance types. |
| Software Dependencies | Yes | We use SCIP 6.0.0 (Gleixner et al., 2018) for the MIP solver. |
| Experiment Setup | Yes | Details on network architectures and hyperparameters are described in Appendix D. ... We use a two hidden layer neural network with Re LU activation (32 units in the first layer and 16 units in the second layer) for both the Q-function and the action function. ... A time limit of 60 seconds and a optimality gap limit of 10 4 are used for all experiments. For GA and CEM, a maximum iterations of 20 and a convergence threshold of 10 6 are used for all experiments if not stated otherwise. Table 7: Hyper parameters settings for CAQL and NAF. Table 8: Hyper parameters settings for DDPG, TD3, SAC. |