This a Unity project of multi-tanks scenes , which is used to train with MARL algorithms.
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Updated
Sep 24, 2021 - C#
This a Unity project of multi-tanks scenes , which is used to train with MARL algorithms.
Using MADDPG for solving Multi Agent Based Unity Environment
🦾 Utilizing a Deep Deterministic Policy Gradient algorithm to train robotic simulations in continuous action space
Implementation of "A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets"
Using Reinforcement Learning Multi-Agent version of DDPG (Deep Deterministic Policy Gradient) algorithm to teach 2 agents how to play tennis
Pytorch-based package for multi-agent reinforcement learning in an iterated prisonner dilemma setting
Blogsite for Interactive Multi-Agent Reinforcement Learning project under the Intelligence Research Group at Computer Society, IEEE NITK Student Branch
PettingZoo ConnectFour and TicTacToe examples, configured with Rye as dependency manager
Ensuring trust among agents using Multi-Agent Deep Reinforcement Learning
A minimalist multi-agent implementation of the social dilemma problem with governance kernels
Python Implementation of the RoboCup Keepaway suitable for Deep Reinforcement Learning.
This project is a computer simulation of a multi-agent extended prisoner’s dilemma using manipulation. The aim is to investigate if the outcome for all agents is better with or without the possibility of manipulation.
Two deep-reinforcement learning agents that play tennis.
Use of deep reinforcement learning (Double DQN-C51) for mobility optimization in wireless sensor networks to generate fairly accurate maps on a tracked phenomenon modeled by a Gaussian Process
A research platform to develop Cyberdefense Multi-Agent Systems combining Multi-Agent-Reinforcement Learning to assist designers to find a suited organization regarding constraints and goals
Enabling UAVs to navigate corridors efficiently, aiming to minimize travel time to their destinations.
Code for my Bachelor's thesis "Learning to Play Pommerman with Emergent Communication" at LMU
Intelligent Social Systems and Swarm Robotics Lab (IS3R)
Adapting to unseen partners in multi-agent Reinforcement Learning (MARL) using Evolutionary Strategies (ES).
An implementation of DDPG agent to solve a Unity environment like Reacher and Crawler.
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