DQN

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20
Apr
DDPG and TD3

This post assumes that you have a strong understanding of the basics of Reinforcement Learning, MDP, DQN and Policy Gradient Algorithms. You can go through Policy Gradients to understand the derivation for Stochastic Policies In the previous post on Actor Critic, we saw the advantage of merging Value based and Policy based methods together. The […]

Posted in: Reinforcement Learning, Robotics,
Tags: Actor Critic, Actor Critic method, Bellman equation, DDPG, Deep Deterministic Policy Gradients, deterministic policy, Double DQN, DQN, Experience Replay, exploration vs exploitation, Fixed D Targets, policy gradients, Q learning, TD3 RL, Twin Delayed Double Deterministic Policy Gradients,
13
Apr
A Summary of Model-Free RL Algorithms

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Reinforcement Learning (RL) refers to training agents with help of incentive-driven environments. RL typically involves a tuple of <state, action, reward> paradigm, which means that the agent has action choices to make in various states, and each action entails a potential reward. This also means that each state has a “value” […]

Posted in: Reinforcement Learning, Robotics,
Tags: Actor Critic, Actor Critic method, AI lab, cartpole, DDPG, deep Q learning, deep Q network, Deep Reinforcement Learning, deterministic policy, DQN, DRL course, gaming, markov decision process, markov process, Markov Reward Process, mdp, model-based RL, model-free RL, monte carlo, off-policy, on-policy, policy based methods, policy gradients, PPO, Q learning, Q table, Rainbow method, Reinforcement learning course, reward function, RL course, RL for gaming, RL training, SAC, stochastic policy, TD Learning, TD3, training in reinforcement learning, TRPO, Value-based methods,
06
Feb
Policy Gradients – An Introduction

I conducted an Introductory session on Reinforcement Learning Policy Gradients (PG) at CellStrat AI Lab on 1st Feb 2020. The goal of this session was to explain the basic underlying principle of Policy Gradients. The session started off with a quick recap of Reinforcement Learning, so that the audience is well aware of the definitions […]

Posted in: Reinforcement Learning,
Tags: Acrobot, deep Q network, deepmind, DQN, experience replay buffer, fixed Q targets, gradient ascent, openAI Gym, policy based methods, policy gradients, Q learning, reinforcement learning, reward function, rewards based learning, value iteration algorithm,
16
Oct
Meeting Minutes from AI Lab session on Saturday 12th Oct in Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday saw some amazing sessions on advanced AI at the CellStrat AI Lab meetup. Diabetes prediction with Machine Learning :- First came a superb presentation by Dr Purnendu Das on diabetes prediction using ML. Dr Das started by discussing the data sources for healthcare and clinical data analysis. He covered the […]

Posted in: Computer Vision, Deep Learning, Healthcare, Machine Learning, Reinforcement Learning,
Tags: deep Q learning, deep Q network, diabetes mellitus, diabetes prediction, DQN, Healthcare, healthcare predictions, Q learning, reinforcement learning, style art, style transfer,
28
May
MEETING MINUTES FROM SATURDAY 25TH. MAY – AI LAB SESSION IN BENGALURU

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars We had another round of deep AI sessions last Saturday in BLR AI Lab meetup. I started with a detailed deep-dive on the Maximum Likelihood Estimation (MLE) algorithm for Logistic Regression, which is a type of classification technique. Logistic Regression has a concave optimization curve wherein we try to maximize the Likelihood […]

Posted in: Deep Learning, Machine Learning, Reinforcement Learning,
Tags: blood cell type detection, CNN, deep learning, deep Q learning, Doom game, DQN, image classification, machine learning, maximum likelihood estimation, object detection, reinforcement learning, RL,