Alexandru Lita's repositories
super-mario-bros-v0
An Reinforcement Learning agent designed to learn and complete OpenAI Gym Super Mario Bros environment. These environments allow 3 attempts (lives) to make it through the 32 stages in the game. The environments only send reward-able game-play frames to agents.
mountain-car-v0
A car is on a one-dimensional track, positioned between two "mountains". The goal is to drive up the mountain on the right; however, the car's engine is not strong enough to scale the mountain in a single pass. Therefore, the only way to succeed is to drive back and forth to build up momentum.
cartpole-v1
A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole is more than 15 degrees from vertical, or the cart moves more than 2.4 units from the center.
house-prices-prediction
Housing prices are an important reflection of the economy, and housing price ranges are of great interest for both buyers and sellers. Property experts make their house price predictions generally for the year ahead, sometimes for a couple of years.
lunar-lander-v2
Landing pad is always at coordinates (0,0). Coordinates are the first two numbers in state vector. Reward for moving from the top of the screen to landing pad and zero speed is about 100..140 points. If lander moves away from landing pad it loses reward back. Episode finishes if the lander crashes or comes to rest, receiving additional -100 or +100 points. Each leg ground contact is +10. Firing main engine is -0.3 points each frame. Solved is 200 points. Landing outside landing pad is possible. Fuel is infinite, so an agent can learn to fly and then land on its first attempt. Four discrete actions available: do nothing, fire left orientation engine, fire main engine, fire right orientation engine.
maze-generator
A maze can be generated by starting with a predetermined arrangement of cells with wall sites between them.
maze-v0
Maze game is a video game genre description first used by journalists during the 1980s to describe any game in which the entire playing field is a maze. Quick player action is required to escape monsters, outrace an opponent, or navigate the maze within a time limit. After the release of Namco's Pac-Man in 1980, many maze games followed its conventions of completing a level by traversing all paths and a way of temporarily turning the tables on pursuers.
multi-armed-bandit
The Multi-armed bandit problem is one of the classical reinforcements learning problems that describe the friction between the agent's exploration and exploitation.
taxi-v3
There are 4 locations (labeled by different letters) and your job is to pick up the passenger at one location and drop him off in another. You receive +20 points for a successful dropoff, and lose 1 point for every timestep it takes. There is also a 10 point penalty for illegal pick-up and drop-off actions.