#Sim-to-Real

Reinforcement Learning Primer September 3, 2026

Reinforcement Learning Basics — MDPs, Bellman Equations, and Exploration for Robots

Reinforcement learning is a closed loop in which an agent chooses actions from observations and maximizes delayed rewards. This primer explains MDPs, value functions, policies, Bellman equations, exploration versus exploitation, reward design, and the path from simulation to a real robot.

Fundamentals · 6 min Reinforcement Learning Primer September 3, 2026

Model-Based Reinforcement Learning and Sim-to-Real

World models, prediction error, MPC, domain randomization, system identification, and safety monitoring for real machines.

Fundamentals · 4 min Reinforcement Learning Primer September 3, 2026

Policy Gradients, PPO, and SAC — Stable Continuous Control for Robots

Policy gradients update a policy directly so that steering, thrust, and joint torque can remain continuous. This article connects Actor-Critic, PPO, and SAC, then covers clipping, entropy regularization, evaluation, and the safety boundary for Sim-to-Real transfer.

Fundamentals · 5 min