Quick Summary of the Video
This third video in the Isaac Lab Reinforcement Learning Series introduces the direct workflow for reinforcement learning (RL) environments. It covers:
- Direct vs. Manager-Based Workflow
- Direct workflow offers fine-tuned control by manually scripting rewards, resets, and observations.
- Unlike the manager-based approach, it does not rely on manager classes for modularity.
- Cart-Pole RL Setup
- The same cart-pole task is used for consistency.
- The goal is to create a Gymnasium registry for connecting RL libraries like skrl and PPO (covered in the next video).
- Environment Configuration
- Defining action space, observation space, and state space.
- Setting up joint indices, physics step rates (120 Hz), and rendering (60 Hz).
- Enabling multi-environment setups by cloning environments and spacing them evenly.
- Markov Decision Process (MDP) Components
- Actions: Sampled from a Gaussian distribution and applied as forces.
- Observations: Joint positions and velocities stored in a dictionary for policy input.
- Rewards:
- Positive reward for staying alive.
- Negative rewards for termination and deviations.
- Penalty terms to discourage excessive movement.
- Termination Conditions:
- Exceeding step limits.
- The cart moving beyond ±3m.
- The pole tilting past 90°.
- Execution & Training Setup
- The environment resets positions and velocities at each episode reset.
- The script will be launched with
train.pyto begin RL training (covered in the next video).
By the end, viewers understand how to manually implement an RL environment in Isaac Lab, gaining full control over environment scripting while preparing for Gymnasium integration and reinforcement learning algorithms in future tutorials.