D 8000 eep Reinforcement Learning track
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Updated
Dec 25, 2023 - Jupyter Notebook
8000
D 8000 eep Reinforcement Learning track
An implementation of deep reinforcement learning to learn and play ATARI games
A reinforcement learning object detector leveraging saliency ranking, offering a self-explainable system with a fully observable action log. | B.Sc. IT (Hons) Artificial Intelligence Dissertation | UOM Dean's List Awards 2024
RL-powered red team simulation in realistic Windows AD enterprise networks — training AI to outsmart cyber defenses safely and at scale.
DQN stock-trading agent with a custom Gymnasium environment and yfinance data.
Project of COMP4125 in 2024-2025.
A simulator with Gymnasium environments, for testing and developing multi-agent system algorithms.
Repository of several ML algorithms implemented for different environments and robot control vectors.
This environment is a modification of the CliffWalking Toy Text environments from Gymnasium. Monster cliff walking involves crossing a gridworld from start to goal while avoiding falling off a cliff and meeting a Monster
A Liquid RL framework for Autonomous Cyber Defence
Implementing DeepQNetwork and Q learning on gymnasium CartPole-V1 env.
A production-ready Deep Q-Learning implementation built entirely from scratch with PyTorch, featuring experience replay, target networks, real-time training visualization, and comprehensive testing framework.
Geschäftsordnung des Elternbeirats am Schickhardt-Gymnasium in Herrenberg
A simple gymnasium environment where the agent must find the correct combination of symbols to unlock a 4-discs padlock.
Gymnasium environment for semiconductor manufacturing simulation 🏭
Solving RL appliance on different gymnasium enviroments, with different learning methodologies
Documentation and implementation of a Reinforcement Learning approach designed to automate inverse kinematics for manipulation tasks involving the arms of SoftBank Robotics' Pepper robot.
🧠 A reinforcement learning project using Stable-Baselines3 and Gymnasium to train agents on classic control tasks.
A Deep Reinforcement Learning agent trained to solve the optimal trade execution problem, reducing costs by over 40% against industry benchmarks.
Implementation and comparison of uniform cost, A* and IDA* search algorithms for solving the Gymnasium Taxi-v3 problem.
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