Principle:Isaac sim IsaacGymEnvs Policy Rollout
| Sources | Domains | Last Updated |
|---|---|---|
| IsaacGymEnvs, common_player.py | Inference, Evaluation | 2026-02-15 00:00 GMT |
Overview
Process of executing a trained neural network policy in a simulated environment to evaluate its performance and collect episode statistics.
Description
Policy rollout involves iteratively querying the trained model for actions given observations, stepping the environment, and collecting rewards and episode metadata. The rollout can run deterministically (greedy actions) or stochastically (sampling from the policy distribution). For visualization, the Isaac Gym viewer renders the simulation in real-time.
The core inference loop follows a standard pattern:
obs = env.reset()
for step in range(max_steps):
action = model(obs) # query policy network
obs, reward, done, info = env.step(action)
accumulate(reward, info)
if all_done:
break
In the vectorized setting of IsaacGymEnvs, all environments run simultaneously on the GPU. Episodes that terminate are automatically reset, and per-episode statistics are collected from the info dict on done signals.
Key rollout parameters:
- n_games -- number of complete episodes to collect before stopping
- max_steps -- maximum steps per episode (safety bound)
- is_determenistic -- whether to use greedy (argmax) or stochastic (sampled) actions
Usage
When evaluating a trained policy's performance or generating visualizations. Rollout produces per-episode reward totals and step counts that quantify policy quality.
Theoretical Basis
Inference loop -- the fundamental RL evaluation pattern: obs -> model(obs) -> action -> env.step(action) -> next_obs, repeated for n_games episodes. The separation of deterministic vs. stochastic evaluation allows users to assess both the expected behavior (greedy) and the robustness (sampled) of the trained policy.