#Reinforcement Learning

Reinforcement Learning Primer September 4, 2026

Multi-Agent Reinforcement Learning Primer — Optimizing in a World Where the Other Side Is Learning Too

When multiple agents learn at the same time, a single-agent MDP no longer holds. This article organizes non-stationarity, cooperative/competitive/mixed settings, CTDE, the credit-assignment problem, and MADDPG and QMIX, with equations and diagrams.

Fundamentals · 8 min Reinforcement Learning Primer September 4, 2026

Reward Design Primer — Why "What to Maximize" Is the Hardest Part of RL

In reinforcement learning implementations, reward-function design more often determines the outcome than the algorithm does. This article organizes sparse vs. dense reward, the policy invariance of potential-based reward shaping, real cases of reward hacking, inverse reinforcement learning, and constrained RL.

Fundamentals · 8 min Technology Trends September 4, 2026

Technology Trends in World Models

A 'World Model' learns the dynamics of an environment and simulates the future inside its own head. From Ha & Schmidhuber's VAE+RNN, through DreamerV3, Genie 3, and Sora, to the JEPA architecture championed by Yann LeCun — this piece maps the split between generative and non-generative approaches, and the shared wall of physical consistency both sides run into.

Trends · 8 min Reinforcement Learning Primer September 3, 2026

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.

Fundamentals · 5 min 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 Technology Trends August 20, 2026

Technology Trends in Navigation

From the classical pipeline of maps, costmaps, and path planning, to end-to-end approaches via reinforcement learning, to foundation models built on Vision-Language models. Surveying where autonomous mobile robot navigation stands today.

Trends · 7 min