176 datasets found

The dataset used in this paper is a multimedia search engine dataset, which is a Self-Learning Search Engine (SLSE) architecture based on reinforcement learning.

The dataset used in the paper is a set of waypoints and edges for planning.

The dataset used in the paper is a 2D environment where experiments are done.

Variational Quantum Circuits are being used as versatile Quantum Machine Learning models. Some empirical results exhibit an advantage in supervised and generative learning...

The Bank Heist environment is a 2D maze with four rooms, where the objective is to navigate to banks distributed across the four mazes.

The MNIST environment does not elicit any actions from an agent. Instead, the prediction network simply needs to learn one step mappings between pairs of MNIST handwritten digits.

The dataset used in the paper is a set of Pong variants, including Noisy, Black, White, Zoom, and others.

The dataset used in the paper is a set of 3D maze games, including Labyrinth and others.

Evolution of Rewards for Food and Motor Action by Simulating Birth and Death

The dataset used in the paper is for the Double Tunnel environment, which is a safety-critical task.

A new Pong environment with a much higher degree of configurability than the current standard, including the ability to compete against a human opponent.

Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. The authors...

The dataset used in the paper is a collection of data from the MuJoCo continuous control tasks.

The dataset used for training the SAI agent, containing 7x7 Go games with multiple komi values.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used MuJoCo environments from the OpenAI gym.

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

The dataset used in the paper is a collection of oracle feedback, which is used to learn a blind spot model of the target world.

The dataset used for the maze exploration task, containing a 9x9 maze with walls and reward locations.

The dataset used in this paper is a set of seven continuous control locomotion tasks implemented in Brax, derived from standard RL benchmarks.

The dataset used in the paper is a collection of tasks from OpenAI Gym, specifically Pong and Breakout.