Teaching Robots to Slide Objects: TQC Reinforcement Learning on FetchSlideDense-v4
you will learn how to train a Truncated Quantile Critics (TQC) agent on the FetchSlideDense-v4 environment using Stable-Baselines3 and Gymnasium Robotics.
Solving the MuJoCo HumanoidStandup Task with PPO and Stable-Baselines3
We learn to solve MuJoCo HumanoidStandup problem using PPO with the help of stable baseline.
Low Light Image Enhancement Using Zero-DCE
Tutorial on Low light image enhancement usind Zero-DCE
Face Detection and Recognition with Python, OpenCV, and Deep Learning: A Step-by-Step Guide
Face Detection and Recognition with Python.
The process involved using MTCNN for face detection, InceptionResnetV1 to extract face embeddings, and an SVM classifier to identify the person based on those embedding
Azure MLOps for Beginners: Train, Deploy and Serve a GRU forecasting model.
Azure MLOps for Begineers: Train, Deploy and Serve a GRU forecasting model.
Build an Image Classifier on Azure: VGG16, Hymenoptera Data, and Flask Deployment
Build an Image Classifier on Azure
Guide to Retinal Blood Vessel Segmentation with U-Net in PyTorch
Learning Image Segmentation with U-Net in Pytorch
Bipedal Walker with PPO: A Step-by-Step Guide to Solving the RL Challenge
In this tutorial we will learn how to master a Bipedal Walker with PPO (Proximal Policy Optimization).
Master Snake Game AI with PPO: Step-by-Step Guide (Part II)
Second Part we will learn about the major components PPO for ai agent.
Prioritized Experience Replay Using PyTorch
Introduction of Prioritized Experience Replay and its implementation with PyTorch.
Policy Gradient Implementation Using PyTorch
This is an implementation of Policy Gradient algorithm using PyTorch.
Gaussian Double Deep Q Learning
Implementation of Gaussian Double Deep Q network with PyTorch
MoG-DQN Implementation In PyTorch To Solve Lunar Lander
This is implementation of MoG-DQN using PyTorch.
Acrobot-ing with Actor Critic
Solving the Acrobot problem with the help of Actor-Critic algorithm.
CNN and its friends in PyTorch
The blog about the CNN functions in PyTorch and other assisting functions generally used with Convolution Neural Networks.
Double Deep Q-Network
In double DQNs, we use a separate network to estimate the target rather than the prediction network. The separate network has the same structure as the prediction network. And its weights are fixed for every T episode (T is a hyperparameter we can tune), which means they are only updated after every T episode. The update is simply done by […]
Climbing the Mountain with Neural Network
Function Approximation For problems with very large number of states it will not be feasible for our agent to use table to record the value of all the action for each state and make its policy accordingly. In Function approximation agent learns a function which will approxmately give it best action for particular state. In this example we will use […]
SARSA in the Wind
We will use SARSA algorithm to find the optimal policy so that our agent can navigate in windy world. SARSA State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. SARSA focuses on state-action values. It updates the Q-function based on the following equation: Q(s,a) = Q(s,a) + α (r + γ Q(s’,a’) – Q(s,a)) Here s’ […]
Balancing pole with Policy Gradient
The policy gradient algorithm trains an agent by taking small steps and updating the weight based on the rewards associated with those steps at the end of an episode. The technique of having the agent run through an entire episode and then updating the policy based on the rewards obtained is called Monte Carlo policy gradient. The action is selected […]