Reinforcement Learning | Poke

Reinforcement Learning @UCZvMhJ3EaNvpacdlMmm3VKA@youtube.com

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Tutorial 1 - Probability Basics 2

Tutorial 2 - Linear Algebra 2

RL Framework and Applications

Introduction to Immediate RL

Bandit Optimalities

Value Function Based Methods

Introduction to RL

Tutorial 1 - Probability Basics 1

Tutorial 2 - Linear Algebra 1

POMDP Introduction

MAXQ Value Function Decomposition

Option Discovery

Hierarchical Abstract Machines

Learning with Options

Semi Markov Decision Processes

Types of Optimality

Hierarchical Reinforcement Learning

Policy Gradient with Function Approximation

REINFORCE (cont'd)

Actor Critic and REINFORCE

Policy Gradient Approach

DQN and Fitted Q Iteration

LSPI and Fitted Q

Function Approximation and Eligibility Traces

State Aggregation Methods

Linear Parameterization

Function Approximation

Backward View of Eligibility Traces

Eligibility Trace Control

Eligibility Traces

Lec 33 - Q-Learning

Thompson Sampling

Lec 34 - Afterstate

Control in Monte Carlo

Dynamic Programming

Policy Iteration

Value Iteration

Lpi Convergence

Convergence Proof

Banach Fixed Point Theorem

Lec 20 - Cauchy Sequence and Green's Equation

Bellman Optimality Equation

Bellman Equation

Full RL Introduction

Contextual Bandits

Thompson Sampling

Returns, Value functions and MDPs