An adaptive Machine Reinforcement Learning (MRL) system is being developed to gather and analyze media data using web scraping, training models to predict outcomes in areas like stock market trends, sports events, and other performance domains. It continuously refines its strategies based on real-time data and evolving patterns.

AI agents for Trackmania using the TMRL package. Implemented DDPG, PPO, and used two SAC algorithms (with one or two critics) to train cars to navigate custom-built tracks.

This repository implements the use of AI for robot tasks.

Training robots to play soccer

The project presents a drone obstacle avoidance system using Microsoft AirSim and the DDPG algorithm, training drones with LIDAR and depth sensors for improved real-time navigation. It compares the implementation of DDPG algorithm with different sensors and their combination.

Controlling a 7 DOF manipulator from the panda gym reacher environment using DDPG

Code for paper "基于多智能体深度强化学习的车联网通信资源分配优化"

Implementation of Deep Deterministic Policy Gradient Algorithm in Pytorch - Using OpenAI-Gym

Robot arm control using reinforcement learning algorithms : DDPG and TD3 with hindsight experience replay (HER)

Implementation of the deep-RL algorithm DDPG.

A multi-agent DDPG that learns to play competitive games in a Unity environment

Reinforcement Learning Project using DDPG

Implimenting DDPG Algorithm in Tensorflow-2.0

Chargym simulates the operation of an electric vehicle charging station (EVCS) considering random EV arrivals and departures within a day. This is a generalised environment for charging/discharging EVs under various disturbances (weather conditions, pricing models, stochastic arrival-departure EV times and stochastic Battery State of Charge (BOC…

Implementation of the deep-RL algorithm DDPG.

Designing a control system to exploit model-free deep reinforcement learning algorithms to solve a real-world autonomous driving task of a small robot.

An implementation of DDPG agent to solve a Unity environment like Reacher and Crawler.