Best Books on Deep Reinforcement Learning for Robotics

Four deep RL books compared for robotics work: Sutton & Barto, Lapan, Morales, and Kober & Peters — plus the reading order that actually works.

Best Robotics Simulation Software Compared (MuJoCo, PyBullet, Isaac Gym)

Compare MuJoCo vs PyBullet vs Isaac Gym for robotics simulation: contact physics accuracy, learning curve, GPU-parallel RL training, and the Isaac Lab successor.

Training AI Agents in Minecraft with Reinforcement Learning

Train AI agents in Minecraft with reinforcement learning: MineRL setup, VPT video pretraining, hierarchical RL, and Voyager LLM-driven planning.

Path Planning with Reinforcement Learning for Mobile Robots: Mapless Navigation and Hybrid Architectures

Deep RL path planning for mobile robots: mapless navigation, costmap observations, reward shaping, dynamic obstacles, and hybrid RRT-plus-RL architecture.

Training a Quadruped Robot to Walk with RL

Train a quadruped to walk with RL in Isaac Lab: 4,096 environments, terrain curriculum, domain randomization, teacher-student distillation, sim-to-real.

Behavior Cloning vs Reinforcement Learning: Robotics Approaches Compared

Behavior cloning vs reinforcement learning for robotics: covariate shift, DAgger, diffusion policies, AMP/GAIL, and today's practical BC-then-RL workflow.

Hindsight Experience Replay: Learning from Failed Attempts

Hindsight Experience Replay explained: relabel failed trajectories with achieved goals to learn from sparse rewards, HER strategies, SAC + HER training.

Isaac Gym Tutorial: NVIDIA's GPU-Accelerated Robot Simulator

Isaac Gym tutorial: GPU-accelerated robot simulation with thousands of parallel PyTorch environments, vectorized rewards, PPO, and domain randomization.

PyBullet Tutorial: Physics Simulation for Robot Learning

PyBullet tutorial for robot learning: GUI vs DIRECT modes, URDF loading, joint control, a Gymnasium reaching environment, PPO training, cameras, sim-to-real.

Building a Custom Gymnasium Environment from Scratch

Build a custom Gymnasium environment from scratch: action and observation spaces, reset and step, termination vs truncation, rewards, registration, and testing.