15 datasets found

The dataset used in the paper is a MuJoCo environment, with 13-states and 4-control inputs, nonlinear dynamics with polynomial dependency in the control inputs.

The dataset used in the paper is a collection of data from the MuJoCo continuous control tasks.

The dataset used in the paper is a continuous control tasks, specifically the Hopper and Half-Cheetah tasks.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used MuJoCo environments from the OpenAI gym.

The dataset used in the paper is the MuJoCo AntGoal environment, which is a variant of the AntGoal environment that uses sparse rewards.

The dataset used in the paper is a set of MuJoCo environments with noise augmentation.

The dataset used in the paper is the Real-World RL Challenge dataset. It contains a set of continuous control tasks.

The dataset used in the paper is the DeepMind Control Suite and Real-World RL Experiments. It contains a set of continuous control tasks based on MuJoCo.

PyBullet is a python module for physics simulation for games, robotics and machine learning.

The DeepMind Control Suite is a collection of 20 robotic manipulation tasks, each with 5 different environments and 5 different robot parameters. The tasks are designed to test...

D4RL benchmark dataset, which consists of four offline logging datasets, collected by different one or mixed behavior policies.

D4RL datasets for maze2d-umaze, maze2d-medium, maze2d-large, antmaze-umaze, antmaze-medium, antmaze-large, parking, soccer-sim, and soccer-physical tasks

The dataset used in the paper is the MuJoCo benchmark, which is a collection of robotic manipulation tasks.

The dataset used in the paper is a collection of simulated environments, including blockworld, canada, cart, cartpole, cartpoleBalance, cartpoleParallelDouble,...

A multi-agent soccer environment with continuous simulated physics.