Omniverse Isaac Gym is a GPU-based physics simulation platform developed by NVIDIA. This open-source toolkit implements various Reinforcement Learning benchmarks, simulating real-world robotic applications.
139 PAPERS • 8 BENCHMARKS
MO-Gymnasium is an open source Python library for developing and comparing multi-objective reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Essentially, the environments follow the standard Gymnasium API, but return vectorized rewards as numpy arrays.
7 PAPERS • NO BENCHMARKS YET
The MoCapAct dataset contains training data and models for humanoid locomotion research. It consists of expert policies that are trained to track individual clip snippets and HDF5 files of noisy rollouts collected from each expert, including proprioceptive observations and actions.
3 PAPERS • NO BENCHMARKS YET
RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established. This is a dataset accompanying the paper RL Unplugged: Benchmarks for Offline Reinforcement Learning.
2 PAPERS • NO BENCHMARKS YET