태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
An open-source toolkit from OpenAI that implements several Reinforcement Learning benchmarks including: classic control, Atari, Robotics and MuJoCo tasks.
(Description by Evolutionary learning of interpretable decision trees)
(Image Credit: OpenAI Gym)
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OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It includes environment such as Algorithmic, Atari, Box2D, Classic Control, MuJoCo, Robotics, and Toy Text. Source: https://github.com/openai/gym Image Source: https://medium.com/@tuzzer/cart-pole-balancing-with-q-learning-b54c6068d947
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. (Image Credit: NVIDIA-Omniverse)
A benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. Source: A Benchmark Environment Motivated by Industrial Control Problems
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.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.