Reinforcement Learning Explained

Reinforcement Learning

The Art of Learning Through Trial and Error

What is Reinforcement Learning?

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. Unlike supervised learning, RL doesn't require labeled input/output pairs, and unlike unsupervised learning, it focuses on maximizing rewards.

flowchart LR A[Agent] -->|Action| B[Environment] B -->|State, Reward| A style A fill:#7dd3fc,stroke:#0ea5e9 style B fill:#f0f9ff,stroke:#0ea5e9

Key Components

  • • Agent: The learner/decision maker
  • • Environment: World the agent interacts with
  • • State: Current situation of the agent
  • • Action: What the agent can do
  • • Reward: Feedback from environment
  • • Policy: Strategy the agent employs

Real-World Examples

  • • Game playing (AlphaGo, Chess AI)
  • • Robotics control
  • • Autonomous vehicles
  • • Recommendation systems
  • • Resource management

How Reinforcement Learning Works

The agent learns through trial and error. At each step, it:

flowchart TD A[Observe State] --> B[Choose Action] B --> C[Receive Reward] C --> D[Update Policy] D --> A style A fill:#7dd3fc,stroke:#0ea5e9 style B fill:#7dd3fc,stroke:#0ea5e9 style C fill:#7dd3fc,stroke:#0ea5e9 style D fill:#7dd3fc,stroke:#0ea5e9

Interactive RL Demo

Start Goal

Key Concepts in RL

Reward Hypothesis

All goals can be described by the maximization of expected cumulative reward.

R = r₁ + γr₂ + γ²r₃ + ...

Where γ (gamma) is the discount factor (0 ≤ γ ≤ 1)

Exploration vs Exploitation

The agent must balance trying new things (exploration) with using known good actions (exploitation).

Explore
Exploit

Markov Decision Process (MDP)

The mathematical framework for modeling RL problems with:

flowchart LR S[State] -->|Action| A A -->|Transition Probability| S' S' -->|Reward| R style S fill:#7dd3fc,stroke:#0ea5e9 style A fill:#7dd3fc,stroke:#0ea5e9 style S' fill:#7dd3fc,stroke:#0ea5e9 style R fill:#7dd3fc,stroke:#0ea5e9

The Markov Property: The future depends only on the present state, not the past.

Types of RL Algorithms

Value-Based

Learn value function V(s) or Q(s,a)

  • • Q-Learning
  • • Deep Q Networks (DQN)
  • • SARSA

Policy-Based

Directly learn policy π(a|s)

  • • REINFORCE
  • • Policy Gradients
  • • Actor-Critic

Model-Based

Learn model of environment

  • • Dyna-Q
  • • Monte Carlo Tree Search
  • • Model Predictive Control

Challenges in RL

Credit Assignment

Determining which actions led to rewards in long sequences.

A
B
C
R
?

Sparse Rewards

Rewards might be rare, making learning difficult.

0
0
0
0
1

© 2023 Reinforcement Learning Explained