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Multi-Agent Games Using Adaptive Feedback Control
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... -
Desk Cleanup environment
The Desk Cleanup environment, which consists of a robotic arm and several blocks on a desk. -
Fetch Manipulation environment with 3 blocks
The Fetch Manipulation environment with 3 blocks, which consists of a robotic arm with a gripper and three blocks. -
Fetch Manipulation environment with 2 blocks
The Fetch Manipulation environment with 2 blocks, which consists of a robotic arm with a gripper and two blocks. -
Fetch Manipulation environment
The Fetch Manipulation environment built on top of Mujoco, which consists of a robotic arm with a gripper and square blocks. -
Breaking the Deadly Triad with a Target Network
The dataset used in the paper "Breaking the Deadly Triad with a Target Network" for training and testing the proposed algorithms. -
Reinforcement Learning with Convex Constraints
The dataset used in the paper is a reinforcement learning problem with arbitrary convex constraints. -
Breakout, SeaQuest, Space Invaders, and Qbert Environments
The dataset used in this work is the Breakout, SeaQuest, Space Invaders, and Qbert environments. -
Atari Environment
The dataset used in this work is the Atari environment in OpenAI Gym, created by the Arcade Learning Environment (ALE). -
Dataset #1 and Dataset #2
Two real-world datasets provided by Cainiao Network, each containing parcel data and constraints configuration data. -
A Deep Reinforcement Learning Approach for Online Parcel Assignment
The online parcel assignment problem, which is aimed at assigning each incoming parcel to a candidate route for delivery, in order to minimize the total cost under consideration... -
Fetch environment in OpenAI Gym
The dataset used in the experiments is the Fetch environment in OpenAI Gym. -
Automatic Curricula via Expert Demonstrations (ACED)
ACED constructs a curriculum by sampling states from expert demonstration trajectories as initializations for each training episode, where the samples initially come from near... -
OpenAI Gym Reacher Environment
The dataset used in the paper is a set of data collected from the OpenAI Gym Reacher environment. -
Motor Babbling Data
The dataset used in the paper is a set of motor babbling data, which is used to initialize the dynamics model and to optimize the control policies. -
Google Research Football Environment
A synthetic dataset generated using Google Research Football Environment for training and testing the proposed tracking method. -
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Rese...
OmniSafe is a comprehensive infrastructural framework designed to accelerate Safe Reinforcement Learning research.
