---
license: apache-2.0
task_categories:
- reinforcement-learning
tags:
- mujoco
- gymnasium
- robotics
- benchmark
- ppo
- sanskrit
pretty_name: MuJoCo SOTA Benchmark
size_categories:
- n<1K
---

# MuJoCo SOTA Benchmark

Standard MuJoCo continuous control benchmarks from [Gymnasium](https://gymnasium.farama.org/) used to evaluate reinforcement learning algorithms.

## Benchmark Environments

| Environment | Obs Dim | Act Dim | CleanRL SOTA | ParamTatva Best |
|---|---|---|---|---|
| **Hopper-v5** | 11 | 3 | 2,382 +/- 271 | **3,183.2** (134%) |
| **Walker2d-v5** | 17 | 6 | ~4,000 | **4,918.5** (123%) |
| **HalfCheetah-v5** | 17 | 6 | ~6,000 | **5,803.9** (97%) |
| **Reacher-v5** | 8 | 2 | ~-4 | **-4.2** (~100%) |
| **Ant-v5** | 27 | 8 | ~5,000 | 886.6 (training) |
| **Humanoid-v5** | 348 | 17 | ~5,000 | 573.8 (training) |

## How to Evaluate

```bash
pip install torch gymnasium[mujoco]
```

```python
import torch
import gymnasium as gym

# Load checkpoint
checkpoint = torch.load("hopper_v5_sota.pt")
agent = Agent(obs_dim=11, act_dim=3)
agent.load_state_dict(checkpoint["model_state_dict"])

# Evaluate
env = gym.make("Hopper-v5")
returns = []
for ep in range(100):
    obs, _ = env.reset()
    total = 0
    done = False
    while not done:
        action = agent.get_action(torch.FloatTensor(obs))
        obs, reward, term, trunc, _ = env.step(action.detach().numpy())
        total += reward
        done = term or trunc
    returns.append(total)

print(f"Mean: {sum(returns)/len(returns):.1f}")
```

## Reference

- Model: [ParamTatva/sanskrit-ppo-hopper-v5](https://huggingface.co/ParamTatva/sanskrit-ppo-hopper-v5)
- Blog: [The 371 Wall](https://huggingface.co/ParamTatva/sanskrit-ppo-hopper-v5/blob/main/blog/MultiTask_Policy_Bottleneck.md)
- CleanRL Baselines: [vwxyzjn/cleanrl](https://github.com/vwxyzjn/cleanrl)

## License

Apache 2.0

ParamTatva.org 2026