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Pong Variants
The dataset used in the paper is a set of Pong variants, including Noisy, Black, White, Zoom, and others. -
3D Maze Games
The dataset used in the paper is a set of 3D maze games, including Labyrinth and others. -
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Le...
The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games. -
Diagnosing Bottlenecks in Deep Q-Learning Algorithms
The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games. -
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. -
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic -
ML4H Findings Track Collection: Machine Learning for Health (ML4H) 2023
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. -
Atari 2600 games
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... -
AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Expe...
Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay -
Dual Policy Distillation
The dataset used in the paper is a continuous control task dataset. -
Prioritized Sequence Experience Replay
Prioritized Sequence Experience Replay (PSER) is a novel framework for prioritizing sequences of transitions to both learn more efficiently and effectively. -
Transactions on Machine Learning Research
The dataset used in the Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning paper. -
An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms
A Deep Reinforcement Learning framework for task arrangement in crowdsourcing platforms. -
PyBulletGym tasks
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... -
Visualizing MuZero Models
MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model. -
Deep Attention Recurrent Q-Network
The Deep Attention Recurrent Q-Network (DARQN) algorithm was tested on several popular Atari 2600 games: Breakout, Seaquest, Space Invaders, Tutankham, and Gopher. -
Human-level control through deep reinforcement learning
The dataset contains data from human-level control through deep reinforcement learning. -
Atari Learning Environment
The dataset used in this paper is the Atari Learning Environment (ALE) dataset, which consists of 15 Atari video games.
