12 datasets found

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

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.

The Pendulum swing-up task, the agent tries to keep the pendulum upright and balanced under the constraint of keeping away from unsafe angles. In the Reacher task, the robotic...

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...