Tabular methods for reinforcement learning
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Jul 3, 2020 - Python
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Tabular methods for reinforcement learning
algorithm reinforcement-learning q-learning reinforcement-learning-algorithms sarsa gridworld policy-evaluation policy-iteration value-iteration reinforcement-learning-agent tabular-q-learning gridworld-environment sarsa-learning q-learning-vs-sarsa cliffwalking gridworld-cliff tabular-environments sarsa-algorithm tabular-methods q-learning-algorithm
PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method
reinforcement-learning q-learning dqn reinforcement-learning-algorithms continuous-control naf ddpg-algorithm prioritized-experience-replay normalized-advantage-functions q-learning-algorithm n-step-bootstrapping
This repository contains the code for automatically generating piano fingerings using a reinforcement learning agent that uses Q-Learning.
Open-zero is a research project aiming to realize the various projects of the company DeepMind
reinforcement-learning pytorch a3c deepmind chess-ai gym-environment a3c-algorithm q-learning-algorithm
Turn based strategy game with AI
The objective is to teach robot to find and reach the target object in the minimum number of steps and using the shortest path and avoiding any obstacles such as humans, walls, etc usinf reinforcement learning algorithms.
reinforcement-learning robotics deep-reinforcement-learning policy-gradient webots ddpg-algorithm deterministic-policy-gradients deepq-learning epuck-robot q-learning-algorithm
Implementation of Q-learning to solve GridWorld
Q-Learning Based Pathfinding in Dynamic Grid Environments
Q-learning application to find an optimal parking slot
reinforcement-learning optimization pillow python-3 agent-based-modeling dynamic-programming hyperparameter-tuning car-parking q-learning-algorithm
A reinforcement learning agent with reflection capabilities for dynamic maze navigation. Implements dual memory system, real-time adaptation, and environment change detection. Open source with research papers and documentation.
machine-learning reinforcement-learning ai q-learning dynamic-environments meta-learning q-learning-algorithm maze-navigation reflection-agent
Implementation of Q-Learning, Double Q-Learning, and LSPI for pricing American options under the Black-Scholes model
The implementation for the paper Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis // NeurIPS 2022
reinforcement-learning q-learning reinforcement-learning-algorithms optimal-control naf reinforcement-learning-environments q-learning-algorithm
Two intelligent agents (cat and mouse) compete with each other to achieve their goal. Agents are trained through reinforcement learning (Q-learning).
reinforcement-learning pygame artificial-intelligence reinforcement-learning-agent reinforcement-learning-environments q-learning-algorithm
This repository contains a Jupyter Notebook with an implemenation of a Q-Learning Agent, which learns to solve the n-Chain OpenAI Gym environment
Deep Q Learning blackbox strategies for casino games
machine-learning deep-neural-networks reinforcement-learning deep-learning tensorflow blackjack deep-reinforcement-learning torch reinforcement-learning-algorithms deep-q-network mlx rlx q-learning-algorithm muzero
Demonstration of Q-Learning and SARSA algorithms utilizing Python and OpenAI GYM
python machine-learning reinforcement-learning q-learning sarsa-learning q-learning-vs-sarsa sarsa-algorithm q-learning-algorithm
a Python-based platformer infused with Q-Learning and dynamic level creation from simple JSON files.
python machine-learning reinforcement-learning machine-learning-algorithms q-learning epsilon-greedy reinforcement-learning-algorithms game-ai reinforcement-learning-playground reinforcement-learning-environments q-learning-algorithm platformer-game
Docking robot in a grid environment trained it with Q-learning
đšī¸ Welcome to Game-Optimization, a repository dedicated to exploring and implementing various optimization algorithms to solve complex games. This project initially focuses on solving the classic game Sokoban using the Q-learning algorithm, with plans to extend to genetic algorithms and other optimization techniques in the future.
RL-guided ETL remediation agent with schema-drift detection, error classification, Q-learning decisions, synthetic benchmarks, and AWS Lambda deployment templates.
reinforcement-learning q-learning etl-pipeline mlops drift-detection q-learning-algorithm mlops-workflow mlops-project schema-drift
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