Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications
InstructorLazy Programmer Team
The curriculum provides structured paths from theoretical concepts to specific code implementations for various RL algorithms.
Instruction covers essential mathematical components including Value Functions and the Bellman Equation.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the history of AI in Go, noting AlphaGo's victory over Lee Sedol and how AlphaZero achieved superhuman play without human strategy.
This slide categorizes video games by era, citing examples like Pong, Doom, and Starcraft II, alongside corresponding gameplay visuals.
This slide contrasts how human infants learn to walk from scratch versus how AIs can copy programs or use simulations to accelerate learning without physical damage.
The course includes specific exercise prompts and the ability to build custom environments from scratch.
A pre-update review from March 2024 noted that the dedicated stock trading project contains basic code that may not function effectively; however, the displayed update date does not prove this has been corrected.
A pre-update review from March 2024 noted that the dedicated stock trading project contains basic code that may not function effectively; however, the displayed update date does not prove this has been corrected.
The curriculum aligns well with the requirement for advanced mathematical and programming prerequisites.