Choose the world, the task, and the learner separately. Isaac Sim provides a robotics simulation platform; Isaac Lab adds robot-learning workflows; MuJoCo supplies another physics engine and ecosystem. The full simulation lab shows how they connect.
For local control experiments, consider MuJoCo. For sensor-rich scenes, investigate Isaac Sim. For batched robot learning in that ecosystem, investigate Isaac Lab. Validate the choice with your actual model, hardware, and evaluation task.
Explore the interactive stack and comparison →Articulated dynamics, actuators, and contact. A compact starting point for CPU control experiments; MJX adds accelerated options with backend-specific capabilities.
Try it: Run a force-controlled carriage, inspect its trajectory, and then replace the controller with a policy.
Create scenes, simulate physical interactions and sensors, render observations, and integrate robotics software. Simulation itself does not train a neural network.
Try it: Drop a dynamic cube onto a ground plane, inspect its height, then add the sensors your robot needs.
Define tasks, rewards, observations, actions, resets, and batches of environments. Connect those environments to a compatible RL training library.
Try it: Train a Franka reaching task with an official runner, then evaluate on held-out target poses.
A network can choose actions, approximate dynamics, correct a physical model, or supplement a controller. Each arrangement learns a different function.
Try it: Fit a neural correction for missing drag and compare its rollout against an incomplete physics model.
A deterministic actor proposes actions; a Q critic evaluates them. Replay reuses experience and slow target networks provide bootstrapped targets.
Try it: Follow the critic's action gradient and calculate one actor and target-network update.
Independent workers collect short rollouts and update a shared stochastic policy and value function. The worker schedule is separate from the physics engine.
Try it: Compute n-step returns and advantages, including the difference between termination and a rollout cutoff.
Keep the comparison honest. Match robot parameters, action units, observation processing, physics and control rates, and evaluation conditions. A photorealistic image, high throughput, or a low training loss answers only part of the question. Compare DDPG and A3C, and choose an experiment →
See the wider tool landscape: Gazebo, Webots, Gymnasium, and more →