2048 environment for Reinforcement Learning and DQN algorithm
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Updated
May 27, 2022 - Python
2048 environment for Reinforcement Learning and DQN algorithm
Deep Reinforcement Learning based Decision-Making in Autonomous Driving Tasks
Deep Reinforcement Learning with Double Q-learning
This is an implementation of Deep Reinforcement Learning for a navigation task. Specifically, DQN algorithm with experience replay method is used to solve the task.
Hybrid Multi-Agent Simulation and Reinforcement Learning framework for financial market forecasting, featuring diverse rule-based traders and a Deep Q-Network trading agent.
Exploring the fundamentals of reinforcement learning (RL) to build agents capable of navigating complex real-world environments and enhancing the training of large language models (LLMs)
A reinforcement learning project exploring different RL algorithms. Namely: QLearning, DQN, PPO, TreeQN, SAVE,
Implemented a Rainbow DQN with Prioritized Experience Replay for Atari games (Space Invaders, CartPole), achieving more efficient learning, faster convergence, and higher performance than traditional DQN.
Simple breakout game with DQN agent which learn how to play it.
A Streamlit application demonstrating Reinforcement Learning (RL) for intelligent product recommendations in online advertising. Explore different RL algorithms and their impact on personalization.
DQN trained snake game running on an ESP32S3 8x8 LED Matrix Board.
# FreeHoopRLThis project uses a Deep Q-Network (DQN) algorithm to train an AI agent for shooting basketballs in a simple 2D environment. The agent learns to choose the right angle and force to score points, with results visualized through training and analysis graphs. 🎉🤖
"Introduction to Reinforcement Learning" course at the Catholic University of Eichstätt-Ingolstadt
Creating a simulation where car learns to drive while minimizing the collisions through RL
DQN_for_CartPole-v1(Based on stable baselines3)
A virtual reality Tic-Tac-Toe built with A-Frame where AI player trained using reinforcement learning to compete against humans.
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