Cart Pole - Gym Documentation

This environment is part of the Classic Control environments. Please read that page first for general information. Action Space Discrete(2) Observation Shape (4,) Observation High [4.8 inf 0.42 inf] Observation Low [-4.8 -inf -0.42 -inf] Import gym.make("CartPole-v1") This environment corresponds to the version of the cart-pole problem described by Barto, Sutton, and Anderson in “Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problem”. A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The pendulum is placed upright on the cart and the goal is to balance the pole by applying forces in the left and right direction on the cart. The action is a ndarray with shape (1,) which can take values {0, 1} indicating the direction of the fixed force the cart is pushed with. Num Action 0 Push cart to the left 1 Push cart to the right Note: The velocity that is reduced or increased by the applied force is not fixed and it depends on t

This environment is part of the Classic Control environments. Please read that page first for general information. Action Space Discrete(2) Observation Shape (4,) Observation High [4.8 inf 0.42 inf] Observation Low [-4.8 -inf -0.42 -inf] Import gym.make("CartPole-v1") This environment corresponds to the version of the cart-pole problem described by Barto, Sutton, and Anderson in “Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problem”. A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The pendulum is placed upright on the cart