Day #156 - Reinforcement Learning

"Rewards for right moves, Starve for wrong moves"

Key Summary

• Intelligence Systems Stack
• Agents to Effectors
• Raw Data - Features - Gain Knowledge - Reason - Short term and Long Term Actions
• Sensory Data - Create Representations
• Raw Sensory Data - Feature Learning (Higher Order Representations) - Extract Actionable usable Knowledge
• Supervised learning - Memorizers
• Reinforcement learning - brute force reasoning
• Reinforcement learning components (Goal - State - Actions - Reward)

Step 1 - Reinforcement Learning Stack

Step 2 - Data Sources

Step 3 - Feature Extraction

Step 4 - Representations

Step 5 - Reasoning

Step 6 - Actions

Types of Deep Learning

Reinforcement Learning Components

Learning States Logic

Markov Decision Process

• State - Action - Reward - State
• Policy - Behavior function
• Value Function - How good is state / function
• Model - Agents representation of Environment
• Stochastic System (having a random probability distribution or pattern that may be analysed statistically but may not be predicted precisely)
• Reward structure changes the next step strategy
• Encourage Exploration with positive reward
• Goal is to Optimize reward

Summary
Intelligence - Ability to accomplish complex goals
Understanding - Ability to turn complex information into simple, useful information

DQN - Deep Q Learning

• Neural Network injected into Q
• Q function injected into Neural Network
• Deep Mind uses DQN
• Greedy way pick the best action

Policy Gradients

• DQN - Q Learning - Off Policy
• Policy Gradient - Directly optimizing policy space

DeepStack

• To beat poker players

"Deep Learning for Perception tasks but not for forming actions"

Happy Mastering DL!!!