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Incentivizing Exploration in Atari
The Incentivizing Exploration in Atari dataset is a collection of 49 Atari games. -
Arcade Learning Environment
The Arcade Learning Environment (ALE) dataset is a collection of 49 Atari games. -
Grid-world environment
The dataset used in the paper is a grid-world environment, which is a discrete MDP. The environment has four walls, some obstacles, a start-state and a reward-state. The goal of... -
New York Road Network
The dataset used in the paper is a real-world traffic signal control dataset, which includes 48 intersections in the New York road network. -
Jinan Road Network
The dataset used in the paper is a real-world traffic signal control dataset, which includes 12 intersections in the Jinan road network. -
Shenzhen Road Network
The dataset used in the paper is a real-world traffic signal control dataset, which includes 33 traffic signals in the Shenzhen road network. -
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. -
BabyAI-PickUpDist-v0
The dataset used in the paper is the BabyAI environment 'BabyAI-PickUpDist-v0' with a one-pickup-per-episode wrapper. -
SHARING LIFELONG REINFORCEMENT LEARNING KNOWLEDGE VIA MODULATING MASKS
The CT-graph and Minigrid environments are used to evaluate lifelong reinforcement learning approaches. -
Pybullet Multigoal
A dataset for robotic manipulation tasks, including ChestPush, ChestPickAndPlace, and BlockStack. -
Atari 2600 domain
The Atari 2600 domain dataset, used for training and testing reinforcement learning algorithms. -
Real-World RL Challenge
The dataset used in the paper is the Real-World RL Challenge dataset. It contains a set of continuous control tasks. -
DeepMind Control Suite and Real-World RL Experiments
The dataset used in the paper is the DeepMind Control Suite and Real-World RL Experiments. It contains a set of continuous control tasks based on MuJoCo.
