Learning agents in oligopolies (Cournot / Stackelberg) Agent-based model
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Updated
May 28, 2024 - C++
Learning agents in oligopolies (Cournot / Stackelberg) Agent-based model
The program uses the DDPG algorithm and tf_agents library to train an agent in a custom environment called "TargetSeeker"
Reinforcement Learning Project using DDPG
Usage of Unity ML-Agents train two agents to play tennis
Tennis Game play using Multi Agent DDPG - Deep Reinforcement Learning
An attempt to detect and prevent DDoS attacks using reinforcement learning. The simulation was done using Mininet.
The DDPG algorithm incorporates Actor-Critic Deep Learning Agent for solving continuous action reinforcement learning problems.
DDPG algorithm applied for the double-jointed arm that can move to target locations.
Important Note fastrl version 2 is being developed at fastrl. Note the link in the readme
Basic implementation of continuous control agents trained using deep reinforcement learning. Project 2 of Udacity Deep Reinforcement Learning NanoDegree.
Implementation of Policy Gradient Methods for Continuous and Discrete Action Spaces
Implementations of Rl algorithms ranging from Q-learning to Multi-Agent RL using DDPG in unity and gym environments.
La combinación más inteligente de Deep Q-Learning, Políticas de Gradiente, Actor-Crítico y DDPG utilizando PyTorch
Learn how to apply reinforcement learning methods to applications that involve multiple, interacting agents. These techniques are used in a variety of applications, such as the coordination of autonomous vehicles.
Create and train a double-jointed arm agent that is able to maintain its hand in contact with a moving target
Reason8.ai PyTorch solution for NIPS RL 2017 challenge
Implementation of the DDPG algorithm for Optimal Finance Trading
Teach a Quadcopter How to Fly!
DDPG Implementaion on bare tensorflow
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