Mountain Car-v0

GPTKB entity

Statements (47)
Predicate Object
gptkbp:instance_of gptkb:stadium
gptkbp:bfsLayer 3
gptkbp:bfsParent gptkb:stadium
gptkbp:analyzes Graphical interface in Open AI Gym
gptkbp:based_on Classic control problem
gptkbp:challenges Deep learning models
Beginner RL agents
gptkbp:depicts Graphs and plots
gptkbp:designed_by gptkb:Open_AI
gptkbp:difficulty gptkb:tank
gptkbp:emulation Continuous time simulation
gptkbp:has_gameplay_element Discrete action space with 3 actions
gptkbp:has_goal Reach the flag at the top of the hill
gptkbp:has_method Physics-based dynamics
gptkbp:has_variants Different versions with modified parameters
https://www.w3.org/2000/01/rdf-schema#label Mountain Car-v0
gptkbp:is_analyzed_in Data scientists
gptkbp:is_available_in Open AI Gym library
gptkbp:is_compatible_with Various RL libraries
gptkbp:is_documented_in Open AI Gym documentation
gptkbp:is_evaluated_by Performance metrics like average reward
Reward-based evaluation
gptkbp:is_explored_in Machine learning practitioners
Reinforcement Learning research
gptkbp:is_implemented_in gptkb:Library
Gym library
Numpy and Matplotlib
gptkbp:is_observed_in Continuous observation space with position and velocity
gptkbp:is_part_of gptkb:software_framework
gptkb:Open_AI_Gym_Classic_Control
Open AI Gym's collection of environments
Reinforcement learning benchmarks
gptkbp:is_popular_in Academic settings
gptkbp:is_similar_to gptkb:Cart_Pole-v0
gptkbp:is_tested_for Researchers and developers
Other environments
gptkbp:is_used_for Algorithm comparison
Testing control algorithms
gptkbp:is_used_in Educational purposes
Benchmarking RL algorithms
gptkbp:number_of_episodes Maximum of 200 steps
gptkbp:prize_money Reward of -1 for each time step until goal is reached
gptkbp:requires Agent to learn to control a car
gptkbp:state Car starts at a random position
Position and velocity of the car
Vector representation of state
gptkbp:training Learning curve varies by algorithm