<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Blogs on Mohamad H. Danesh</title><link>https://modanesh.github.io/blog/</link><description>Recent content in Blogs on Mohamad H. Danesh</description><generator>Hugo -- 0.147.2</generator><language>en</language><lastBuildDate>Thu, 09 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://modanesh.github.io/blog/index.xml" rel="self" type="application/rss+xml"/><item><title>Heterogeneous Environments in Isaac Lab</title><link>https://modanesh.github.io/blog/hetero-isaaclab/</link><pubDate>Thu, 09 Apr 2026 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/hetero-isaaclab/</guid><description>A technical guide to training universal robot control policies across multiple morphologies</description></item><item><title>Domain Randomization</title><link>https://modanesh.github.io/blog/domain-randomization/</link><pubDate>Tue, 23 Dec 2025 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/domain-randomization/</guid><description>Bridging the Reality Gap - A Survey of Domain Randomization and Future Horizons</description></item><item><title>Ablation Study of the Bayesian GAN</title><link>https://modanesh.github.io/blog/b_gan/</link><pubDate>Mon, 13 Feb 2023 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/b_gan/</guid><description>Exploring the capacity and limitation of the Bayesian GAN.</description></item><item><title>Generating Gaussian Samples From A Uniform Distribution</title><link>https://modanesh.github.io/blog/gauss_uniform/</link><pubDate>Wed, 16 Mar 2022 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/gauss_uniform/</guid><description>Generating numbers that are distributed with the Gaussian distribution (with any mean and standard deviation as parameters), starting from the random number generator of a computer, i.e. the rand() function.</description></item><item><title>Distributional Reinforcement Learning</title><link>https://modanesh.github.io/blog/c51-qrdqn-iqn/</link><pubDate>Wed, 03 Mar 2021 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/c51-qrdqn-iqn/</guid><description>Presenting some of the most fundamental works on distributional RL.</description></item><item><title>Actor-Critic with Experience Replay</title><link>https://modanesh.github.io/blog/actor-critic-with-experience-replay/</link><pubDate>Fri, 29 Jan 2021 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/actor-critic-with-experience-replay/</guid><description>A brief overview of the ACER RL algorithm is provided.</description></item><item><title>Exploration and Generalization in Reinforcement Learning</title><link>https://modanesh.github.io/blog/exp_gen/</link><pubDate>Mon, 14 Sep 2020 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/exp_gen/</guid><description>A brief description on a few methods to make RL agents explore and generalize faster/better.</description></item><item><title>Summary: Artificial Intelligence - A Modern Approach</title><link>https://modanesh.github.io/blog/ai_summary/</link><pubDate>Thu, 02 Apr 2020 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/ai_summary/</guid><description>My understandings and notes from the AI modern approach book.</description></item><item><title>Summary: Mathematics for Machine Learning</title><link>https://modanesh.github.io/blog/math_ml/</link><pubDate>Wed, 01 Apr 2020 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/math_ml/</guid><description>My understandings and notes from the Math for ML book.</description></item><item><title>Reinforcement Learning Key Papers Keynotes</title><link>https://modanesh.github.io/blog/rl-key-papers-key-notes/</link><pubDate>Sun, 01 Dec 2019 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/rl-key-papers-key-notes/</guid><description>Keynotes from teh RL Key Papers of Spinning Up by OpenAI.</description></item><item><title>Convolutional Neural Network Explanation Methods</title><link>https://modanesh.github.io/blog/conv-neur-net-explanations/</link><pubDate>Tue, 19 Nov 2019 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/conv-neur-net-explanations/</guid><description>A brief description on explanations methods in the computer vision literature.</description></item><item><title>Automatic Environment Generation to Generalize Agents</title><link>https://modanesh.github.io/blog/auto_envs/</link><pubDate>Thu, 16 May 2019 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/auto_envs/</guid><description>Using GANs and evolution algorithms to generate a curriculum for the RL agent.</description></item><item><title>RL Course by David Silver Notes</title><link>https://modanesh.github.io/blog/rl-course-by-david-silver-notes/</link><pubDate>Fri, 14 Dec 2018 00:00:00 +0000</pubDate><guid>https://modanesh.github.io/blog/rl-course-by-david-silver-notes/</guid><description>After being excited about RL for more than a year, I should have a concise and satisfying answer to the question, &amp;#39;What is reinforcement learning?&amp;#39; Here it is gathered briefly.</description></item></channel></rss>