Reinforcement Learning Primer

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September 3, 2026 Reinforcement Learning Primer

Reinforcement Learning Primer: Imitation Learning and Inverse Reinforcement Learning

Behavior Cloning, DAgger, and inverse reinforcement learning use demonstrations when rewards are hard to write. This primer covers covariate shift, reward inference, VLA connections, evaluation, safety, and data provenance.

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.

September 3, 2026 Reinforcement Learning Primer

Q-Learning and DQN — From a Q-Table to Deep Reinforcement Learning

Q-learning updates action values with the Bellman optimality target. This primer follows the path from a tabular Q-table to a Deep Q-Network, explaining experience replay, target networks, overestimation, and safe placement in a robot stack.