Implementation of some Deep Reinforcement Learning algorithms and environments.
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Updated
Oct 26, 2023 - Python
Implementation of some Deep Reinforcement Learning algorithms and environments.
UC-Berkely Pacman
Container handling problem solved using multi-agent model
Explore foundational AI concepts through the Pac-Man projects, designed for UC Berkeley's CS 188 course. Implement search algorithms, multi-agent strategies, and reinforcement learning techniques in Python, emphasizing real-world applications. Engage in the Eutopia Pac-Man contest for a multiplayer capture-the-flag challenge
This Python tool employs multi-agent routing to efficiently handle diverse tasks: one agent generates QR codes, while another retrieves and processes data from a CSV file. Depending on the user's query, the appropriate agent is dynamically selected to provide accurate responses or actions.
Implementation of projects 0,1,2,3 of Berkeley's AI course
Implementation of Berkeley's Pacman Project as a part of Artificial Intelligence course.
This repository contains the code necessary for generating the figures presented in the paper titled "Cooperative Thresholded Lasso for Sparse Linear Bandit".
Multi-agent communication module for pytorch.
Mini-max, Alpha-Beta pruning, Expectimax techniques are used to implement multi-agent pacman adversarial search.
Contains algorithms to train Pacman agent to find/eat food particles .Multi-Agent Algorithms are also used.
AI project for Principles & Applications of Artificial Intelligence at AUT.
Multi Agent Simulation of a Simple Firearm Debate using Mesa
Leverage the power of multi-agent AI to fuel your daily tech, programming, and architecture insights.
Developing AI search agents to win Pacman.
multi-agent pybox2d environment based off of OpenAI multiagent-particle-envs
An Eclectic and Malleable Multi-Chatbot Framework
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