Dueling Network Architectures for Deep Reinforcement Learning — Review & Introduction Let’s go over some important definitions before going through the Dueling DQN paper. Most of these should be familiar. Given the agent’s policy π, the action value and state value are defined as, respectively: The above Q function can also be written as:

Tackling maximization bias in Deep Q-learning — Introduction In this post, we will look into the motivation behind double Q-learning networks, and look at three different ways this has been done: The original algorithm in “Double Q-learning” (Hasselt, 2010) The updated algorithm from the same author in “Deep Reinforcement Learning with Double Q-learning” (Hasselt et al., 2015), The…

Deep Q Learning Explained — Introduction This post will be structured as followed: We will briefly go through general policy iteration and temporal difference methods. We will then understand Q learning as a general policy iteration. Finally, we will understand and implement DQN presented in Deepmind’s paper “Playing Atari with Deep Reinforcement Learning (Mnih et al…

Reinforcement Learning in Continuous Action Spaces — This post is a thorough review of Deepmind’s publication “Continuous Control With Deep Reinforcement Learning” (Lillicrap et al, 2015), in which the Deep Deterministic Policy Gradients (DDPG) is presented, and is written for people who wish to understand the DDPG algorithm. …

A Reinforcement Learning based Intelligent Physician for Chemotherapy Treatment — There has been increasing interest in research for applying machine learning to medical domains. For instance, computer vision has proved to be a valuable tool for providing analytical information for physicians and medical researchers. I wanted to explore if intelligent machines could directly take on the role of a physician…

Important Concepts in Deep Reinforcement Learning — Preliminaries In my previous post, we derived policy gradients and implemented the REINFORCE algorithm (also known as Monte Carlo policy gradients). There are, however, some glaring issues with vanilla policy gradients: noisy gradients and high variance. But why does that happen? Recall the policy gradient: As in the REINFORCE algorithm, we…

Policy gradient methods are ubiquitous in model free reinforcement learning algorithms — they appear frequently in reinforcement learning algorithms, especially so in recent publications. …