V-MPO

Simple code to demonstrate Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) in Pytorch

Getting Started

This project is using Pytorch for Deep Learning Framework, Gym for Reinforcement Learning Environment. Although it's not required, but i recommend run this project on a PC with GPU and 8 GB Ram

Prerequisites

Make sure you have installed Pytorch and Gym.

• Click here to install gym
• Click here to install pytorch

Installing

Just clone this project into your work folder

git clone https://github.com/wisnunugroho21/reinforcement_learning_v_mpo.git

Running the project

After you clone the project, run following script in cmd/terminal :

Discrete

python discrete.py

Continous

python continous.py

On-Policy adaptation of Maximum a Posteriori Policy Optimization (MPO)

Some of the most successful applications of deep reinforcement learning to chal- lenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting. However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse. As an alternative to policy gradient algo- rithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state- value function. We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters. On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported. V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.

You can read full detail of V-MPO in here