Simulating a Self-Driving Car in Unreal Engine using Reinforcement Learning

This repository implements a Deep Q-Learning algorithm to train an agent to balance a pole on a cart using the CartPole-v1 environment from Gymnasium. The project includes a TensorFlow-based neural network, epsilon-greedy exploration, experience replay, and visualization of the trained agent's performance.

No description available

utilising deep learning and reinforcement learning as Deep Q-networks (dual policy action critic) to train an agent for the cartpole environment

No description available

DQN agent with e-greedy / softmax policy, experience replay and target network.

Deep Q-learning algorithm for my thesis. It should learn to drive a car

This repository contains the implementation of a Deep Q-Network (DQN) designed to solve the Cartpole balancing problem, a classic reinforcement learning task from OpenAI's Gym. The project implements advanced techniques like experience replay and a target network, along with extensive hyperparameter tuning, ablation studies to optimize performance.

No description available

Deep Q Learning project for EE 5885 Deep Reinforcement

No description available

No description available

This project implements a Deep Reinforcement Learning (DRL) agent to solve the MountainCar-v0 environment from the Gymnasium library. The implementation includes core RL components such as experience replay, target networks, environment wrappers, and an enhanced version of Q-learning using Double DQN.

Solving cartpole promlem with deep-q-net reinforcement learning algorithm (simple implementation). Homework for ITMO university.

No description available

A Reinforcement learning agent to move between two points through a dynamic map.

This is a CNN model that with help of reinforcement learning and deepQlearning manages, too make the car in enduro drive fast on the track and identify other cars and overtake them.