25 datasets found

The authors used the Mujoco control tasks, including Ant-v2, HalfCheetah-v2, Hopper-v2, and Walker2d-v2.

A Multi-Agent Reinforcement Learning (MARL) framework for cooperative data collection to build a 3D mapping of the disaster scene using agents present around the incident zone...

The dataset used for the cart-pole problem is a finite set of states: S, a finite set of actions: A, a state transition probability matrix, P, a reward function R, and a...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors conducted experiments on several representative tasks from the OpenAI Gym and...

The dataset used in the paper is a procedurally generated environment called ProcGen.

The Continual World benchmark consists of ten realistic robotic manipulation tasks.

Minigrid environment, a grid-based problem in reinforcement learning.

Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation

The proposed uncertainty-aware model-based reinforcement learning framework is applied to end-to-end autonomous driving tasks.

The Bootstrapped DQN dataset is a collection of 49 Atari games.

The Incentivizing Exploration in Atari dataset is a collection of 49 Atari games.

The Arcade Learning Environment (ALE) dataset is a collection of 49 Atari 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 Rainbow dataset, which is a combination of six extensions to the DQN algorithm.

The dataset used in the paper is the Dynamic Frame Skip Deep Q-Network (DFDQN) dataset, which consists of 3 Atari games: Seaquest, Space Invaders, and Alien.

The dataset used in the paper is the Deep Q-Network (DQN) dataset, which consists of 15 classic Atari games.

The dataset used in the paper is the Proximal Policy Optimization (PPO) dataset, which consists of 10 different Atari environments.

The dataset used in the paper is the OpenAI Gym dataset, which consists of a set of environments for reinforcement learning.

The dataset used in the paper is the Arcade Learning Environment (ALE) dataset, which consists of 57 classic Atari games.

The Playground environment is a continuous 2D world. In each episode, N = 3 objects are uniformly sampled from a set of 32 different object types (e.g. dog, cactus, sofa, water,...