Reinforcement Learning (RL)

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• Markov Decision Process (MDP)
• Monte Carlo (MC) Method - Model Free Reinforcement Learning
• Deep Reinforcement Learning (DRL) - DeepRL
• Neural Architecture Search (NAS) with Reinforcement Learning | Barret Zoph & Quoc V. Le ...Wikipedia
• Distributed Deep Reinforcement Learning (DeepRL)
• Deep Q Learning (DQN)
• Neural Coreference
• State-Action-Reward-State-Action (SARSA)
• Deep Deterministic Policy Gradient (DDPG)
• Trust Region Policy Optimization (TRPO)
• Proximal Policy Optimization (PPO)
• AdaNet

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• Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
• Lifelong Learning
• Dopamine Google DeepMind
  • Math for Intelligence
• Inside Out - Curious Optimistic Reasoning
• World Models
• Google DeepMind AlphaGo Zero
• Google’s AI picks which machine learning models will produce the best results | Kyle Wiggers - VentureBeat off-policy classification,” or OPC, which evaluates the performance of AI-driven agents by treating evaluation as a classification problem
• Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more | Maxim Lapan
• Reinforcement-Learning-Notebooks - A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python

This is a bit similar to the traditional type of data analysis; the algorithm discovers through trial and error and decides which action results in greater rewards. Three major components can be identified in reinforcement learning functionality: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent can do. Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is best achieved when the agent has a good policy to follow. Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn

Q Learning Algorithm and Agent - Reinforcement Learning w/ Python Tutorial | Sentdex - Harrison

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Reinforcement Learning | Phil Tabor

• Code | GitHub

⌨️ (00:00:00) Introduction

⌨️ (00:01:30) Intro to Deep Q Learning

⌨️ (00:08:56) How to Code Deep Q Learning in Tensorflow

⌨️ (00:52:03) Deep Q Learning with Pytorch Part 1: The Q Network

⌨️ (01:06:21) Deep Q Learning with Pytorch part 2: Coding the Agent

⌨️ (01:28:54) Deep Q Learning with Pytorch part 3

⌨️ (01:46:39) Intro to Policy Gradients 3: Coding the main loop

⌨️ (01:55:01) How to Beat Lunar Lander with Policy Gradients

⌨️ (02:21:32) How to Beat Space Invaders with Policy Gradients

⌨️ ([1] 02:34:41) How to Create Your Own Reinforcement Learning Environment Part 1

⌨️ (02:55:39) How to Create Your Own Reinforcement Learning Environment Part 2

⌨️ (03:08:20) Fundamentals of Reinforcement Learning

⌨️ (03:17:09) Markov Decision Processes

⌨️ (03:23:02) The Explore Exploit Dilemma

⌨️ (03:29:19) Reinforcement Learning in the Open AI Gym: SARSA

⌨️ (03:39:56) Reinforcement Learning in the Open AI Gym: Double Q Learning

⌨️ (03:54:07) Conclusion

Gridworld: How To Create Your Own Reinforcement Learning Environments

• Designing Your Own Open Ai Gym Compatible Reinforcement Learning Environment | NEURALNET.AI