[TNNLS] PGDQN: A generalized and efficient preference-guided epsilon-greedy policy equipped DQN for Atari and Autonomous Driving

This repository has RL algorithms implemented using python

Developed various model-based and model-free Intelligent and Naive algorithms for the beam balance environment in OpenAI Gym.

Deep RL for Temporal Credit Assignment in decision processes with delayed rewards

🏓Deep learning model is presented to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards in RL Pong environment.

The simulation of Epsilon-Greedy and Thompson Sampling algorithms for Bayesian A/B Testing. The project shows how both algorithms find the optimal bandit and approximate the rewards of each bandit, given the true reward. Visualizations are done to demonstrate the learning process and convergence.

A Deep Q-Network reinforcement learning model trained to safely land a lunar lander in the Farama Gymnasium

This project implements Value Iteration and Q-Learning algorithms to solve a variety of gridworld mazes and puzzles. It provides pre-defined policies that can be customized by adjusting parameters and policy optimization through iterative reinforcement learning. It also brings exploration capabilities to the agent with Epsilon Greedy Q-Learning.

A mini project during 3 days of Tet Holiday 2023

심층강화학습기반 장애물과 신호등을 고려한 다차선 자율주행 연구