3260 papers • 126 benchmarks • 313 datasets
Image state version of the multi-objective reinforcement learning toy environment originally introduced in "Empirical evaluation methods for multiobjective reinforcement learning algorithms" by P. Vamplew et al.
(Image credit: Papersgraph)
These leaderboards are used to track progress in deep-sea-treasure-image-version-2
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Use these libraries to find deep-sea-treasure-image-version-2 models and implementations
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A deep reinforcement learning realization of the algorithm is introduced and promising results are presented on a standard benchmark for non-linear MORL and a real-world application from the domain of manufacturing process control.
Adding a benchmark result helps the community track progress.