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..
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Authors: Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G Bellemare
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite. |
| Researcher Affiliation | Collaboration | Rishabh Agarwal Marlos C. Machado Pablo Samuel Castro Marc G. Bellemare Google Research, Brain Team EMAIL Also at Mila, Université de Montréal. Now at Deep Mind. |
| Pseudocode | Yes | Algorithm 1 Contrastive Metric Embeddings (CMEs) and J. PSEUDO CODE, including functions like def metric_fixed_point and def contrastive_loss. |
| Open Source Code | No | We use the open-source code released by Sonar et al. (2020) for our experiments. |
| Open Datasets | Yes | Jumping task from pixels (Tachet des Combes et al., 2018), LQR with spurious correlations (Song et al., 2019), and Distracting DM Control Suite (Stone et al., 2021; Zhang et al., 2018b). |
| Dataset Splits | Yes | We split the problem into 18 seen (training) and 268 unseen (test) tasks... For hyperparameter selection, we evaluate all agents on a validation set containing 54 unseen tasks in the wide grid (Figure 2a) and pick the parameters with the best validation performance. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | All agents are built on top of SAC (Haarnoja et al., 2018) combined with Dr Q (Kostrikov et al., 2020)... |
| Experiment Setup | Yes | Table G.2: Common hyperparameters across all methods for all jumping task experiments. Table G.3: Optimal hyperparameters for reporting results in Table 1. Table G.4: Optimal hyperparameters for reporting results in Figure 5.3. Table G.5: Optimal hyperparameters for reporting ablation results in Table 2. |