playing-games-2
Deep-Sea Treasure, Image version
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)
Benchmarks
These leaderboards are used to track progress in deep-sea-treasure-image-version-2
Trend
Dataset
Best Model
Actions
No benchmarks available.
Libraries
iUse these libraries to find deep-sea-treasure-image-version-2 models and implementations
Datasets
No datasets available.
Subtasks
No subtasks available.
Most implemented papers
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach
Johannes Dornheim•Sun Apr 10 2022
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.
5