198 datasets found

A dataset for interactive recommender systems, used to evaluate the proposed Tree-structured Policy Gradient Recommendation (TPGR) framework.

The dataset used in the paper is a high-dimensional state space of the CartPole environment, artificially expanded to 500 dimensions using a union of five different nonlinear...

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

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 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 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 this work is the Breakout, SeaQuest, Space Invaders, and Qbert environments.

ACED constructs a curriculum by sampling states from expert demonstration trajectories as initializations for each training episode, where the samples initially come from near...

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

The dataset used for testing the reinforcement learning agent for medical alarm annotation.

The dataset used for training and testing the reinforcement learning agent for medical alarm annotation.

OmniSafe is a comprehensive infrastructural framework designed to accelerate Safe Reinforcement Learning research.

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.