Independent projects — infrastructure, robotics, technology surveys, and corporate research
Tag · 2 entries

#Sim-to-Real

September 3, 2026 Reinforcement Learning Primer

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.

 September 3, 2026 Reinforcement Learning Primer

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.