29 datasets found

The dataset used in the paper is a 2D environment where experiments are done.

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

The authors used the Mujoco control tasks, including Ant-v2, HalfCheetah-v2, Hopper-v2, and Walker2d-v2.

The Four Rooms domain is a classic reinforcement learning environment where an agent must navigate a grid world to reach one of four goals.

The Desk Cleanup environment, which consists of a robotic arm and several blocks on a desk.

The Fetch Manipulation environment with 3 blocks, which consists of a robotic arm with a gripper and three blocks.

The Fetch Manipulation environment with 2 blocks, which consists of a robotic arm with a gripper and two blocks.

The Fetch Manipulation environment built on top of Mujoco, which consists of a robotic arm with a gripper and square blocks.

The dataset used in the paper is a set of data collected from the OpenAI Gym Reacher environment.

The dataset is used to train the Make-An-Agent model, a novel policy parameter generator that leverages the power of conditional diffusion models for behavior-to-policy generation.

The dataset is used to train the Make-An-Agent model, a novel policy parameter generator that leverages the power of conditional diffusion models for behavior-to-policy generation.

The dataset is used to train the Make-An-Agent model, a novel policy parameter generator that leverages the power of conditional diffusion models for behavior-to-policy generation.

The Continual World benchmark consists of ten realistic robotic manipulation tasks.

Minigrid environment, a grid-based problem in reinforcement learning.

The dataset used in the paper is the Mujoco benchmarking continuous control tasks.

The dataset used in the paper is a set of demonstrations for a robot insertion task, which is a continuous control problem. The demonstrations are collected by a robot...

The dataset used in the paper is a dual-pendulum environment, which is a continuous action space task.

The Habitat dataset is a large-scale indoor simulator dataset containing 145 semantically-annotated indoor scenes.

The dataset is used for training and testing the Equivariant Diffuser for Generating Interactions (EDGI) algorithm.