Reinforcement Learning Course Lecture by Nicholas Chernette

Introduction

• Trainer: Nicholas Chernette
• Course Goal: To progress from beginner to adept at leveraging reinforcement learning
• **Topics Covered: **
  • Setting up environment
  • Working with algorithms
  • Testing and building custom environments

Course Outline

Step 1: RL (Reinforcement Learning) Overview

• How it works: Teaching agents through trial and error
• Key components:
  • Agent
  • Environment
  • Actions
  • Rewards/Observations
• Applications:
  • Autonomous driving (Carla)
  • Securities Trading
  • Neural Network Architecture
  • Robotics
  • Gaming
• Limitations:
  • Overkill for simple problems
  • Environment assumptions
  • Training duration and stability
• Examples:
  • Training models to balance poles, drive cars, etc.

Step 2: Environment Setup

• Tools Required:
  • Open AI Gym
  • Mujoco for real robots
  • Various libraries based on application
• Environment Representation: Spaces
  • Box: Range of values
  • Discrete: Specific set of items
  • Tuples: Combining spaces
  • Dict: Combination of types
  • Multi-binary and Multi-discrete: Specialized spaces
• Environment Testing: Using Open AI Gym
  • Observation/Action Spaces interaction
  • Testing environments (Cartpole example)

Step-by-step Implementation**

Step 3: Training Models

• Model-free RL algorithms: A2C, PPO, DQN, HER, SAC, TD3
• Algorithm Suitability: Based on type of action spaces
  • Discrete
  • Continuous
  • Evaluation Metrics: Rewards, Entropy, etc.
• Setting up/train model: Using libraries like Pytorch with Stable Baselines
• Saving/Loading Models: Methods and techniques for deployment

Step 4: Testing and Evaluation

• Evaluation Policy: Seeing the model’s performance
• Tensorboard: For metric visualization

Step 5: Callbacks, Algorithms, Architectures

• Reward Thresholds: Stop training when threshold reached
• Using Callbacks: To monitor and save best models
• Custom Architectures: Changing neural network setups
• Alternate Algorithms: Implementing various RL algorithms

Projects

Project 1: Reinforcement Learning for Atari Games

• Setup Dependencies: Gym, A2C, vec_env tools
• ROM Installation: For Atari environments
• Implementing Environment: Using gym’s tools
• Training: Using CNN policies and testing via random actions

Project 2: Reinforcement Learning for Autonomous Driving

• Setup Dependencies: Swig, Piglet
• Environment Setup: Box2D racing car
• Training: PPO algorithm
• Testing: Using trained model, adjusting action spaces

Project 3: Custom Environment Creation

• Example: Shower temperature environment
• Setup Dependencies: Imported necessary libraries
• Environment Coding: Defining state, steps, rewards
• Training: PPO algorithm
• Evaluation: Testing and refining model

Summary

• Learning Resources: Courses by experts like David Silva or books by pioneers (Richard Sutton)
• Next Steps:
  • Hyperparameters, detailed environments, end-to-end implementation
• Final tips: Importance of understanding required metrics, training duration, algorithm choices