Cart Pole-v1

GPTKB entity

Statements (62)
Predicate Object
gptkbp:instance_of gptkb:environment
gptkbp:action discrete
move left
move right
gptkbp:available_in Open AI Gym library
gptkbp:community active
gptkbp:created_by gptkb:Open_AI
gptkbp:difficulty easy to moderate
gptkbp:difficulty_levels beginner
gptkbp:environment_type classic control
gptkbp:evaluates average reward
gptkbp:goal balance a pole on a cart
gptkbp:has_applications_in educational purposes
robot control
simulation training
research experiments
algorithm testing
gptkbp:has_variants gptkb:Cart_Pole-v0
gptkb:Cart_Pole-v2
https://www.w3.org/2000/01/rdf-schema#label Cart Pole-v1
gptkbp:influenced_by gptkb:strategy
gptkb:robotics
control theory
gptkbp:introduced_in gptkb:2016
gptkbp:is_a_framework_for gptkb:Tensor_Flow
gptkb:Keras
gptkb:Py_Torch
gptkbp:is_popular_among gptkb:students
gptkb:researchers
gptkbp:is_similar_to gptkb:Mountain_Car-v0
gptkb:Acrobot-v1
gptkbp:latest_version v1
gptkbp:max_episode_steps gptkb:500
gptkbp:mission stabilize the pole
gptkbp:observation_space continuous
gptkbp:part_of gptkb:gymnasium
gptkbp:prize_pool +1 for each time step the pole remains upright
gptkbp:render visualizes the environment
gptkbp:requires gptkb:Matplotlib
gptkb:Python
gptkb:Num_Py
gptkbp:reset initializes the environment
gptkbp:simulation_speed real-time
gptkbp:state 4-dimensional vector
cart position
cart velocity
pole angle
pole angular velocity
gptkbp:step takes an action and returns the next state
gptkbp:supports gptkb:Deep_Q-Networks
Q-learning
Policy Gradients
gptkbp:termination_condition cart position exceeds threshold
pole angle exceeds threshold
gptkbp:tutorials many online resources
gptkbp:type reinforcement learning problem
gptkbp:used_in machine learning research
gptkbp:used_in_competitions AI competitions
gptkbp:used_in_courses AI courses
reinforcement learning courses
gptkbp:bfsParent gptkb:gymnasium
gptkbp:bfsLayer 4