13 datasets found

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

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 DeepMind Control Suite is a collection of 20 robotic manipulation tasks, each with 5 different environments and 5 different robot parameters. The tasks are designed to test...

The dataset used in the paper is a collection of simulated environments, including blockworld, canada, cart, cartpole, cartpoleBalance, cartpoleParallelDouble,...

The CartPole problem is a classic control problem in robotics and control theory. It is a simple, continuous control problem where a pole is attached to a mass on a cart, and...