PACE uses the squared L2 norm of policy parameter changes from a first-order approximation as an efficient proxy for environment value in UED, outperforming baselines with higher IQM and lower optimality gap on MiniGrid and Craftax OOD tests.
Craftax: A lightning-fast benchmark for open-ended reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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Closed-loop prompt-based translation with hierarchical verification and iterative repair produces equivalent high-performance RL environments across five cases including new TCGJax.
Dual-stream excitatory/inhibitory networks trained with modulo error routing achieve 96.7% MNIST and 61.7% CIFAR-10 accuracy plus competitive RL performance, revealing task-dependent credit-assignment bottlenecks under Dale's principle.
The paper presents stable-worldmodel (swm), a platform with high-performance data layer, modern world model baselines, planning solvers, and extended environments for reproducible research and generalization evaluation.
citing papers explorer
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PACE: Parameter Change for Unsupervised Environment Design
PACE uses the squared L2 norm of policy parameter changes from a first-order approximation as an efficient proxy for environment value in UED, outperforming baselines with higher IQM and lower optimality gap on MiniGrid and Craftax OOD tests.
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Automatic Generation of High-Performance RL Environments
Closed-loop prompt-based translation with hierarchical verification and iterative repair produces equivalent high-performance RL environments across five cases including new TCGJax.
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Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks
Dual-stream excitatory/inhibitory networks trained with modulo error routing achieve 96.7% MNIST and 61.7% CIFAR-10 accuracy plus competitive RL performance, revealing task-dependent credit-assignment bottlenecks under Dale's principle.
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stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
The paper presents stable-worldmodel (swm), a platform with high-performance data layer, modern world model baselines, planning solvers, and extended environments for reproducible research and generalization evaluation.