# Simbav2 Continuous Control Eval

> Evaluates the sample efficiency, optimization stability, and scaling capabilities of deep reinforcement learning algorithms across diverse continuous control environments. Use when the user wants to benchmark on MuJoCo, DMC Suite, MyoSuite, HumanoidBench, or asks about evaluating this task. Reports Normalized Return.

- Skill: `qhjqhj00/simbav2-continuous-control-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/simbav2-continuous-control-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/simbav2-continuous-control-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/simbav2-continuous-control-eval

---


# simbav2-continuous-control-eval

> Hyperspherical Normalization for Scalable Deep Reinforcement Learning — Hojoon Lee et al. (arXiv:2502.15280, 2025)

## What this evaluates

Evaluates the sample efficiency, optimization stability, and scaling capabilities of deep reinforcement learning algorithms across diverse continuous control environments.

## Datasets

- **MuJoCo** — total 5; splits: test (-1)
- **DMC Suite** — total 21; splits: test (-1)
- **MyoSuite** — total 10; splits: test (-1)
- **HumanoidBench** — total 14; splits: test (-1)

## Metrics

- `Normalized Return` **(primary)** — range: [0, 1]
  - Domain-specific normalization: MuJoCo returns divided by TD3 baseline, DMC returns divided by 1000, MyoSuite uses success rates, HumanoidBench normalized by success score. Final metric is the average across all 57 tasks.

## Input / output format

**Input**: Continuous state observations from MuJoCo, DMC, MyoSuite, and HumanoidBench environments.

**Output**: Continuous action vectors selected by the policy network.

## Scoring recipe

```python
def normalize_return(env, raw_return):
    if env == 'MuJoCo': return raw_return / td3_baseline
    elif env == 'DMC': return raw_return / 1000.0
    elif env == 'MyoSuite': return success_rate
    elif env == 'HumanoidBench': return raw_return / success_score
    return raw_return

scores = [normalize_return(env, get_final_return(env)) for env in all_57_tasks]
metric_value = mean(scores)
ci = bootstrap_ci(scores, n=1000) # 95% CI over seeds
```

## Common pitfalls

- Normalization baselines differ per domain (TD3 for MuJoCo, 1000 for DMC, success rate for MyoSuite, success score for HBench); using a uniform baseline will skew results.
- The UTD (update-to-data) ratio critically affects performance; high UTD ratios require careful handling of weight reinitialization to avoid overfitting, which varies across algorithms.
- Statistical significance is determined via Welch’s t-test (p=0.05), not standard Student’s t-test.

## Evidence (verbatim from paper)

> To aggregate performance across diverse domains, each environment’s return is normalized to a near $[0,1)$ range. Specifically, MuJoCo performance is normalized by TD3; DMC returns are divided by 1000; MyoSuite scores use success rates; and HumanoidBench scores are normalized by their success score.

## Citation

```bibtex
@misc{lee2025hyperspherical,
  title={Hyperspherical Normalization for Scalable Deep Reinforcement Learning},
  author={Hojoon Lee et al.},
  year={2025},
  note={arXiv:2502.15280}
}
```

- arXiv: 2502.15280