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Extended, multi-agent and multi-objective (MaMoRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.
Explore the Gridworld Simulation 🌍🚀! An agent navigates a 5x5 grid to maximize rewards, using the Value Iteration algorithm 🔄. Visualizations 📊 show optimal paths and value convergence. Dive into dynamic programming and decision-making! 🤖🧠
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.
In this project, we aim to implement value iteration and Q-learning. First, the agents are tested on a Gridworld, then apply them to a simulated robot controller (Crawler) and Pacman. (Source : Berkley's public projects and labs)