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We provide the code repository for our paper This repository includes the necessary code to replicate our experiments and utilize our DRL model for spacecraft trajectory planning. By accessing the repository, researchers and practitioners can benefit from our approach to efficiently transfer spacecraft to GEO using low-thrust propulsion systems.
Reinforcement learning for a custom MuJoCo Hopper with PPO, REINFORCE, and Actor-Critic, featuring domain randomization, curriculum learning, and entropy scheduling for robust locomotion under uncertain dynamics
Implementation of a Reinforcement Learning (RL) model to learn to scratch the less possible surface on the scratch game of the Badulaque of the app "The Simpsons Springfield".
This project involves creating a custom Blackjack environment and training an AI using reinforcement learning techniques, specifically Proximal Policy Optimization (PPO) and Deep Q-Network (DQN). The goal is to teach the AI to play Blackjack and achieve the best possible win rate.
Custom-built Proximal Policy Optimization (PPO) agent learns to master a 2D shooter game. Features from-scratch PPO implementation, Pygame-based environment, and OpenAI Gym integration. Showcases reinforcement learning in game AI, combining advanced algorithm development with practical game design.
Inventory optimization using Deep Reinforcement Learning — DQN and PPO agents trained in a custom Gymnasium environment to minimize holding, stockout, and ordering costs under stochastic demand.