21 datasets found

The dataset used in the paper is a set of Pong variants, including Noisy, Black, White, Zoom, and others.

The dataset used in the paper is a set of 3D maze games, including Labyrinth and others.

The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games.

The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games.

The dataset used in the paper "Breaking the Deadly Triad with a Target Network" for training and testing the proposed algorithms.

DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic

DMLab-30 is a benchmark for multitask reinforcement learning in partially observable environments.

A synthetic dataset for training a family of Reinforcement Learning (RL) methods to build explainable pathways for the differential diagnosis of anemia, as a primary use case.

The dataset used in this paper is a collection of state-action pairs generated by a pre-trained RL agent, used to train a self-supervised interpretable network (SSINet) to...

Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay

The dataset used in the paper is a continuous control task dataset.

Prioritized Sequence Experience Replay (PSER) is a novel framework for prioritizing sequences of transitions to both learn more efficiently and effectively.

The dataset used in the Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning paper.

A Deep Reinforcement Learning framework for task arrangement in crowdsourcing platforms.

The dataset used in the paper is a collection of experiences sampled from a replay buffer, used to train and evaluate the proposed Multi-step DDPG (MDDPG) and Mixed Multi-step...

MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model.

The Deep Attention Recurrent Q-Network (DARQN) algorithm was tested on several popular Atari 2600 games: Breakout, Seaquest, Space Invaders, Tutankham, and Gopher.

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

The dataset contains data from human-level control through deep reinforcement learning.

The dataset used in this paper is the Atari Learning Environment (ALE) dataset, which consists of 15 Atari video games.