328 datasets found

The dataset used in the paper is a set of demonstrations for a robot insertion task, which is a continuous control problem. The demonstrations are collected by a robot...

Autonomous UAV Navigation Using Reinforcement Learning

Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage

Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay

The authors propose a tree-based reinforcement learning approach for optimal water reservoir operation.

The authors propose a risk-averse batch reinforcement learning approach for foreign exchange trading.

The authors propose a lifelong RL approach that learns a hyper-policy, whose input is time, that outputs the parameters of the policy to be queried at that time.

The dataset used in the paper is a reinforcement learning dataset, where the goal is to learn a policy that maximizes the expected return in a Markov decision process.

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

The dataset used in this paper for cellular-connected UAV path design with reinforcement learning.

We develop data-driven reinforcement learning (RL) control designs for input-affine nonlinear systems. We use Carleman linearization to express the state-space representation of...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a symbolic reinforcement learning algorithm to learn end-to-end control...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a symbolic reinforcement learning algorithm to learn end-to-end control...

The MineRL dataset is a large-scale dataset of Minecraft demonstrations, focusing on the development of sample-efficient reinforcement learning algorithms for mining items in...

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

The dataset used in the paper is a collection of reward functions for autonomous driving, which are designed to evaluate the performance of reinforcement learning agents.

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

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 dataset used in the paper is a reinforcement learning dataset, where the authors analyze the modularity of discrete-action reinforcement learning algorithms.

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...