Dream to Control - Learning Behaviors by Latent Imagination

Explore DeepMind's Dreamer, a revolutionary reinforcement learning agent that masters continuous control tasks through forward-imagination in latent space. Delve into the innovative approach of learning world models from high-dimensional sensory inputs using deep learning techniques. Discover how Dreamer efficiently derives behaviors by propagating analytic gradients of learned state values through imagined trajectories in a compact state space. Examine the agent's performance across 20 challenging visual control tasks, where it outperforms existing approaches in data-efficiency, computation time, and final performance. Gain insights into the algorithm, models, learning objectives, dynamics learning, behavior learning, and agency learning that power this groundbreaking AI advancement.

Introduction
Algorithm
Models
Learning Objectives
Dynamics Learning
Behavior Learning
Agency Learning