☰
Notes
🔍
×
Statistics
Supervised Learning
Unsupervised Learning
Deep Learning
Reinforcement Learning
▼
Foundations & Concepts
29
Core Vocabulary
9
Markov Decision Processes
8
Value Functions
6
Returns & Rewards
6
▼
RL Paradigms
15
Learning Approaches
5
Problem Settings
5
Multi-Agent RL
5
Exploration vs Exploitation
9
Multi-Armed Bandits
7
▼
Tabular Methods
24
Dynamic Programming
5
Monte Carlo Methods
6
Temporal Difference Learning
7
Tabular Control Algorithms
6
Function Approximation
7
▼
Deep Reinforcement Learning
25
Value-Based Deep RL
7
Policy Gradient Methods
7
Actor-Critic
4
Trust Region & Modern Algorithms
7
Model-Based RL
8
▼
Reward Engineering & Imitation
9
Reward Design
4
Imitation Learning
5
Extensions & Variants
9
RL Frameworks & Environments
8
Applications
9
AI ML
>
Reinforcement Learni…
>
Foundations & Concep…
Overview
◀
Map
Tree
Terms
Read
▶
Resources
Quiz
Play
Foundations & Concepts
29
terms
Core Vocabulary
9
terms
Agent
Environment
State
Action
Reward
Policy
Time Step
Episode
Trajectory
Markov Decision Processes
8
terms
Markov Decision Process (MDP)
Markov Property
State Space
Action Space
Transition Function
Reward Function
Discount Factor (Gamma)
Horizon
Value Functions
6
terms
Value Function
State-Value Function (V)
Action-Value Function (Q)
Bellman Equation
Bellman Optimality Equation
Optimal Policy
Returns & Rewards
6
terms
Return
Cumulative Reward
Discounted Return
Credit Assignment
Sparse Reward
Dense Reward
Term
Open full page
↗
Loading...