reinforcement learning
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops Recently, I presented an extensive session on Logic and Reasoning at the CellStrat AI Lab. This topic comes under the broader area of Artificial General Intelligence or AGI. This workshop included the following topics :- Symbolic AI Propositional Logic First Order Logic Program Synthesis Relation Network Symbolic AI Although, Deep Learning […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops In recent weeks, I had presented a session on “AlphaZero with Monte Carlo Tree Search” algorithm at the CellStrat AI Lab. This is an algorithm developed by Google Deepmind in 2016. It mastered the game of GO and beat the 18-time world champion at the time Lee Sedol. Go is an ancient Chinese abstract strategy […]
Recently I presented a session on Optimization-based Meta Learning, Part 3 of the Meta Learning Series, at the CellStrat AI Lab. The previous parts are found here – Part 1 (Metric-based Meta Learning), Part 2 (Model-based Meta Learning) Meta Learning, of course, refers to “Learning to Learn“. Quick Recap (Metric-based Meta Learning) Meta-Learning deals with […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops Recently, I presented a session on Metric-based Meta Learning at the CellStrat AI Lab (where I am an AI Researcher). Metric-based Meta Learning might be considered a domain of Artificial General Intelligence (AGI). This is due to the fact that Meta Learning helps us create generalized systems with relatively less data. […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars I presented a session on Multi-Agent RL recently at the CellStrat AI Lab. Introduction :- In the normal Reinforcement Learning setup, you have one agent which interacts with the environment. It uses the Observation from the environment, performs actions and observes the rewards. In real life, many applications will involve several agents […]
This post discusses temporal difference (TD) methods, used in Reinforcement Learning. It contrasts TD methods with Monte Carlo (MC) methods and dynamic programming. You need to have a thorough understanding of Markov Decision Process (MDP) to understand this post. Prediction and Control : In general, RL methods have two components 1) Prediction / Evaluation : where […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Last Saturday (4th Apr ’20), CellStrat AI Lab conducted a Global Code Jam for AI/ML solutions for the current COVID-19 pandemic, which has brought a great deal of issues in the world. With the Code Jam, we tried to solve some of the COVID-19 problems with help of AI / ML […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Minutes from Saturday 14th March 2020 AI Lab Workshop at BLR :- Session Presenter : SHUBHA M., Deep Reinforcement Learning Researcher, CellStrat AI Lab Last Saturday, our Reinforcement Learning Team Lead Shubha M. presented a fantastic presentation and workshop on Actor-Critic method used in RL. She also demonstrated a demo of this technique for Stock Market predictions. […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Minutes from Saturday 7th March 2020 AI Lab meetup at BLR :- Last Saturday, we had excellent sessions in the AI Lab meetup. Face Recognition with MTCNN and FaceNet :- First Amit Kumar presented a detailed overview of Face Recognition with MTCNN and FaceNet. Face Recognition involves a pipeline of Face […]
In my previous post, we discussed the simplest Policy Gradient REINFORCE. We saw, how Policy based methods are better than value based methods, a derivation of the Gradient of Score(Cost) function, and an implementation of simple Policy Gradient to train Gym’s Acrobot-v0. We then saw, how introducing a baseline reduces variance which leads to the […]