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Intro

Part One: Reinforcement Learning (RL)

Applications: Board Games

Applications: 2D Video Games

Applications: Simulated 3D Robotics

Applications: Robotics

Applications: "World Models"

Applications: Language grounding

Applications: Multi-agent collaboration

The Formulation

Agent-Environment Loop in code

Core Concepts: State(s)

Core Concepts: Complex State(s)

Core Concepts: Reward(s)

Core Concepts: Return and Discount → The Return Gt is the total discounted reward from time-stept

Core Concepts: Value Function(s)

Core Concepts: Policies

Core Concepts: Markov Assumption

Core Concepts: Markov Decision Process

Model-based: Dynamic Programming

Model-based Reinforcement Learning

Bellman equation

Policy evaluation example

Generalized Policy Iteration

GridWorlds: Sokoban

The rest of the iceberg

Continuous action/state spaces

Exploration vs Exploitation

Credit Assignment

Sparse, noisy and delayed rewards

Reward hacking

Model-free: Reinforcement Learning

Monte Carlo evaluation

Temporal difference evaluation

Q-learning: Tabular setting

OpenAl gym

DeepMind Lab

Part Two: Deep Reinforcement Learning

Value function approximation

Policy Gradients: Baseline and Advantage

Policy Gradients: Actor-Critic for Starcraft 2

Policy Gradients: PPO for DotA

Policy Gradients: PPO for robotics

Policy Gradients: Sonic Retro Contest

Big picture view of the main algorithms

More RL applications

Description:

Dive into the world of Reinforcement Learning (RL) with this comprehensive talk by Ben Duffy. Explore the evolution of sequential decision making, from achieving superhuman performance in complex board games to solving 2D Atari and 3D games like Doom, Quake, and StarCraft. Gain insights into the pursuit of creating artificial general intelligence and understand the main breakthroughs, paradigms, formulations, and obstacles within RL. Learn about the agent-environment loop, core concepts such as state, reward, value functions, and policies, and delve into model-based and model-free RL approaches. Discover applications in robotics, language grounding, and multi-agent collaboration. Examine deep reinforcement learning techniques, including value function approximation and policy gradients, and their applications in various domains. Get up to speed with the current state of the field and its future directions in this informative one-hour lecture.

Introduction to Reinforcement Learning

Open Data Science

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#Computer Science #Machine Learning #Reinforcement Learning #Artificial Intelligence #Engineering #Robotics #Humanities #Games #Board Games #Deep Reinforcement Learning #Policy Gradient #Model Based Reinforcement Learning