Principle:Isaac sim IsaacGymEnvs Task Testing Iteration

Field	Value
Principle Name	Task Testing and Iteration
Overview	Development cycle for testing a new RL environment by running training, observing behavior, and iteratively refining the reward function and observation design.
Domains	Development, Testing
Related Implementation	Isaac_sim_IsaacGymEnvs_Train_Py_Task_Execution
Last Updated	2026-02-15 00:00 GMT

Knowledge Sources	IsaacGymEnvs Isaac Gym Docs
Domains	Development, Testing
Last Updated	2026-02-15 00:00 GMT

Description

After implementing and registering a custom task, the development process enters an iterative cycle of testing, observation, and refinement. This cycle involves four progressive stages:

Stage 1: Visual Verification (Few Environments, Rendering Enabled)

Run the task with a small number of environments (num_envs=4-16) and rendering enabled (headless=False) to visually verify:

• Assets load correctly: Robot and objects appear in the expected positions and orientations.
• Physics behaves reasonably: Objects do not explode, interpenetrate, or float.
• Actions have effect: Applying random actions produces visible motion in the expected DOFs.
• Resets work: Environments reset to valid initial states when conditions are met.

Stage 2: Reward Signal Verification

Check that the reward function produces meaningful signals:

• Non-zero rewards: Rewards should vary between good and bad states (not constant zero or constant positive).
• Correct sign: Desired behaviors should yield positive rewards; undesired behaviors should yield negative rewards or lower positive rewards.
• Reward scale: Rewards should be in a reasonable range (typically 0-10 per step after scaling). Very large or very small rewards can destabilize training.
• Reward components: Log individual reward components to verify each one activates in the expected situations.

Stage 3: Observation Verification

Verify that observations contain sufficient information for learning:

• State coverage: Print observation buffers and verify all components have non-zero, varying values.
• Information sufficiency: The observations must contain enough information for the agent to determine the optimal action (Markov property).
• Normalization: Check that observation values are in reasonable ranges. Very large values can cause numerical issues.
• No NaN/Inf: Physics instabilities can produce invalid values that propagate through the network.

Stage 4: Full-Scale Training

Scale up to full parallel training and monitor learning curves:

• Reward trend: Mean episode reward should increase over training epochs.
• Episode length: For survival tasks, episode length should increase; for goal-reaching tasks, it should decrease.
• Policy entropy: Should decrease as the agent becomes more confident, but not collapse to zero too quickly.
• Value function accuracy: The value loss should decrease over time.

Theoretical Basis

The testing cycle follows the empirical evaluation loop for RL environment development:

Implement --> Test (small scale) --> Observe (visual + metrics) --> Refine --> Repeat

Key principles from RL debugging literature:

• Start simple, add complexity: Begin with minimal environments and rendering. Only scale up after verifying basic correctness.
• Reward engineering is iterative: It is rare to get the reward function right on the first try. Expect multiple iterations of reward tuning.
• Ablate components: When the agent fails to learn, disable reward components one at a time to identify which one is causing problems.
• Baseline comparison: Compare against known-working tasks (e.g., Cartpole) to verify the training pipeline is functioning correctly.

When to Use

Use this principle when:

• Testing a newly implemented RL environment for the first time.
• Debugging a task where the agent fails to learn or learns the wrong behavior.
• Iterating on the reward function or observation design after initial testing.
• Scaling up from development to full training runs.

Common Issues and Fixes

Symptom	Likely Cause	Diagnostic	Fix
Robot explodes/flies away	Physics instability	Visual inspection with few envs	Reduce sim.dt, increase substeps, check asset joint limits
Agent does not move	Actions not applied correctly	Print forces in pre_physics_step	Verify set_dof_actuation_force_tensor call, check action scaling
Reward is constant	Reward formula error	Print reward components	Fix reward computation, verify state tensors are refreshed
Agent learns wrong behavior	Reward misspecification	Watch trained policy in viewer	Adjust reward weights, add penalty terms
NaN in observations	Physics produces invalid state	Add NaN checks after refresh	Increase solver iterations, add joint limits, reduce forces
Learning plateaus early	Insufficient observations	Review obs_buf contents	Add missing state components (velocities, contacts, goal info)
Very slow learning	Reward too sparse	Plot reward histogram	Add shaping rewards that guide toward desired behavior
Agent exploits reward	Reward loophole	Watch trained policy behavior	Add penalty terms, tighten reset conditions

Development Workflow

1. Start small: python train.py task=MyTask num_envs=8 headless=False max_iterations=10
2. Verify physics: Watch the viewer, check for explosions or instabilities.
3. Check rewards: Monitor reward output, print reward components.
4. Check observations: Print obs_buf values, verify ranges and validity.
5. Short training run: python train.py task=MyTask num_envs=256 max_iterations=100
6. Monitor learning: Check TensorBoard for reward curves.
7. Iterate on rewards: Adjust weights, add components, re-run.
8. Full training: python train.py task=MyTask (default num_envs and max_iterations).
9. Evaluate: python train.py task=MyTask test=True checkpoint=runs/MyTask/nn/MyTask.pth

Related Pages

• Isaac_sim_IsaacGymEnvs_Train_Py_Task_Execution - implements - Concrete commands and API for executing task training and testing.
• Isaac_sim_IsaacGymEnvs_Task_Requirements_Design - feedback loop - Testing results inform design refinements.
• Isaac_sim_IsaacGymEnvs_Task_Registration - prerequisite - Task must be registered before it can be tested.
• Isaac_sim_IsaacGymEnvs_Hydra_Task_Train_YAML - configuration - YAML configs control testing parameters.

Implementation:Isaac_sim_IsaacGymEnvs_Train_Py_Task_Execution