28 datasets found

The dataset used in this paper is a collection of nonlinear robot motion planning problems with integrated perception and control.

A dataset of pouring tasks using model predictive control.

A shutdown problem is a one-agent two-decision SCIM containing a path D1(cid:57)(cid:57)(cid:75) H (cid:57)(cid:57)(cid:75) D2 (cid:57)(cid:57)(cid:75) S...

The dataset used in the paper is the LunarLander environment, which is a classic control problem. The agent learns to land a lunar lander using human feedback.

The dataset used in the paper is the CartPole environment, which is a classic control problem. The agent learns to balance a pole using human feedback.

The dataset is used to test the proposed method for inferring representations of time series.

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

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 generator data dataset contains information about the generators in the IEEE 39 bus system, including their nominal powers and operating ranges.

The dataset used in the paper is an Inverted Pendulum dataset, which is a standard benchmark system in control and reinforcement learning.

The Humanoid Walk dataset is a collection of images of a humanoid robot performing various tasks.

The dataset used in this paper is the PyBullet Environments.

The dataset used in this paper is the DeepMind Control Suite and PyBullet Environments.

The dataset used in this paper is a Cartpole system, where the objective is to prevent the pole from falling over by pushing the cart to the left or to the right.

The dataset used in the paper is the Pendulum dataset, which is a continuous control problem.

The dataset used in the paper is a reinforcement learning dataset, specifically a Markov Decision Process (MDP) with a finite set of states and actions.

The dataset used in this paper is a collection of data points from a lunar lander, which is used to test the proposed APG algorithm for task switching.

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

The dataset used in the paper is a set of environments for reinforcement learning, including CartPole, Pendulum, and LunarLander.

The dataset used for training the dm_control environment.