328 datasets found

MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning

The dataset used in the paper is a reinforcement learning dataset, specifically for time-limited tasks and time-unlimited tasks.

The dataset used for training a robotic agent to play the theremin instrument using time-dependent goals.

The dataset used in the paper is a reinforcement learning environment, specifically the MiniGrid and BabyAI environments.

The dataset used in the paper is a set of environments with different topological properties, including a gridworld, a torus, and a four rooms environment. The agent is tasked...

A realistic simulation environment for training reinforcement learning agents as traffic signal controllers in a medium-sized town in Germany.

The dataset is a power grid simulator built on Grid2Op, an existing simulator and RL interface.

The dataset used in the paper is a grid world navigation task with four actions: up, down, left, or right. Transitions are stochastic and with a 5% probability the agent moves...

The dataset used in the paper is a Continuous MDP with continuous state and action spaces.

The dataset used for the experiments in the paper, consisting of 2D and 3D environments, and objects to interact with.

The dataset is a collection of full episodes of claims records from hundreds of successful past treatments.

The dataset used in this paper is a set of five game-theoretic tasks (Harmony Game, Hawk-Dove, Stag-Hunt, Prisoners Dilemma and Battle of the Exes) with seven different agent...

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 "Breaking the Deadly Triad with a Target Network" for training and testing the proposed algorithms.

The dataset used in the paper is a reinforcement learning problem with arbitrary convex constraints.

The dataset used in this work is the Breakout, SeaQuest, Space Invaders, and Qbert environments.

The dataset used in this work is the Atari environment in OpenAI Gym, created by the Arcade Learning Environment (ALE).