198 datasets found

The Hungry Geese environment is a discrete environment where four geese are placed on a 7x11 grid, and the goal is to collect food while avoiding collisions with other geese.

The Vehicle Routing Problem (VRP) is a classic problem in combinatorial optimization. The problem is to find the shortest route that visits each node in a graph exactly once and...

The Newsvendor problem is a classic problem in inventory management. The problem is to determine the optimal order quantity to satisfy uncertain demand.

The Bin Packing problem is a classic problem in Operations Research and Computer Science. The problem is to pack items of different sizes into bins of fixed capacity.

Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection...

The Cart-Pole environment is a classic control problem in reinforcement learning, where an agent learns to balance a pole using a cart.

Comparison datasets for Imitation Learning and Reinforcement Learning.

Reinforced Imitation Learning (RILe) dataset, which consists of expert demonstrations and noisy expert data.

The dataset used in the paper is the StarCraft Multi-Agent Challenge (SMAC) environment.

The dataset used in the paper is a multiple domain cyberspace attack and defense game model based on reinforcement learning.

MinAtar is a new evaluation platform designed to allow for more thorough and reproducible experiments in reinforcement learning.

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

The dataset used in the paper is the BabyAI environment 'BabyAI-PickUpDist-v0' with a one-pickup-per-episode wrapper.

The CT-graph and Minigrid environments are used to evaluate lifelong reinforcement learning approaches.

The Atari 2600 domain dataset, used for training and testing reinforcement learning algorithms.

The Sungka dataset is a two-player turn-based board game where each player takes turns moving stones to collect as many stones as possible.

The dataset used in the paper is the Real-World RL Challenge dataset. It contains a set of continuous control tasks.

The dataset used in the paper is the Go dataset. It contains a set of Go games.

The Atari dataset is a collection of 55 Atari games, each with its own set of rules and objectives.

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.