First, it uses a replay buffer to store past experiences and we can sample training data from it periodically. This implementation is an advanced Q-learning agent in two aspects. ![]() We used the RLcard DQN agent written in TensorFlow as a base and created a more powerful, more manageable, and easy to use code in Pytorch. The code for the second milestone is a DQN agent in PyTorch. It is used as a presentation that the chosen environment works and the agent is ready to train. The presented code for the first milestone is based on the RLcard github repository example code. ![]() Team members: László Barak, Mónika Farsang, Ádám Szukics After training, we can play against our pre-trained agent. Our project focuses on reinforcement learning with the aim of training an agent in a poker environment. This repository contains the project for the Deep learning class (course code: VITMAV45) at the Budapest University of Technology and Economics.
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