Advanced · Reinforcement Learning

Deep Reinforcement Learning

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The combination of deep learning and reinforcement learning, allowing agents to learn complex tasks directly from raw sensory input.

Technical Definition

The combination of deep learning and reinforcement learning, allowing agents to learn complex tasks directly from raw sensory input.

How it works

Deep Reinforcement Learning (DRL) integrates the powerful feature learning capabilities of deep neural networks with reinforcement learning's framework for decision-making through trial and error. This synergy enables agents to learn optimal policies in complex environments with high-dimensional observations, successfully tackling challenges in robotics, game playing (e.g., AlphaGo), and autonomous systems. It is characterized by agents learning to map raw inputs to actions by maximizing a reward signal.

Related Concepts

• Q-Learning — A model-free RL algorithm that learns action values without knowing environment dynamics.