Yashwanth Reddy Virupaksha's repositories
Policy-Gradients-Contextual-Bandit-Problem
The contextual Bandit problem is the intermediate between Simple Bandit problem and the full RL problem. In this experiment we are going to find optimal policy to obtain maximum rewards.
Docked-protein-Interaction-ranking-using-graph-neural-networks
A deep learning library to rank protein complexes using graph neural networks
CapsuleNet_PokemonClassification
Using Capsule Networks to perform classification of different Pokemon's
Graph-Neural-Networks
A deep learning library for graph data structures
Policy-Gradients-Mulit-armed-Bandit-Problem
With the concept of Policy Gradients in Reinforcement Learning we are going find optimal policy for obtaining maximum reward in Multi-armed Bandit Problem
Q-Table-Learning-OpenAI-Gym
With the concept of Q-Table learning in Reinforcement Learning we are going to experiment in the environment "FrozenLake" provided by OpenAI Gym
autonomous-cars-ND-LaneDetection
Lane detection using computer vision algorithms
Deep_learning_on_databases
Deep Learning models on various databases
Policy-Gradients-Full-RL-CartPole
This experiment learns the optimal policies by the method of Policy-Gradients in the Full Reinforcement Learning problem in the environment "CartPole" from OpenAI Gym.
Q-Learning-Neural-Networks-OpenAI-Gym
With the concept of Q-Learning using Neural Networks in Reinforcement Learning we are going to experiment in the environment "FrozenLake" provided by OpenAI Gym
cs515-001-s20-JandY-pdfsam
PDFsam, a desktop application to extract pages, split, merge, mix and rotate PDF files
Model-and-Policy-Networks-Reinforcement-Learning
The Reinforcement Learning problem can be improved in certain circumstances by creating a Model neural network to learn the dynamics of the real environment and learn by experimenting in the Model environment instead of Real environment.
withai.github.io
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