Github program for reinforcement q learning. Q-values are set state-action pairs and the algorithm chooses an optimal action for the current state based on estimates of this Jul 11, 2025 ยท Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more. Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL) One of the simplest ways of doing Reinforcement Learning is called Q-learning. A simplified attempt, where one player uses classic probabilities, the dealer (house) simply draws until 17, and the adaptive agent uses non-deterministic Q-learning in order to play as best as possible. Especially, This program is to explore a maze. This program utilizes Q-Learning to allow a robot to correctly pic up cans and avoid walls in a grid world environment. We will use Reinforcement Learning to let Peter explore his environment, collect tasty apples and avoid meeting the wolf. Python was used to program two classes which setup the state and agent respectively. It uses pytorch for the neural network and the kRPC mod to control the rocket from the python script. Options: basic Q-learning, Dyna-Q (for model planning), double Q-learning (to avoid maximization bias). Add this topic to your repo To associate your repository with the reinforcement-learning-algorithms topic, visit your repo's landing page and select "manage topics. wo d9 vn5vk kzy vmps ap 8yn soimktww wkxv vryyr