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--- |
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license: apache-2.0 |
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task_categories: |
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- reinforcement-learning |
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tags: |
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- mujoco |
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- gymnasium |
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- robotics |
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- benchmark |
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- ppo |
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- sanskrit |
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pretty_name: MuJoCo SOTA Benchmark |
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size_categories: |
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- n<1K |
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--- |
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# MuJoCo SOTA Benchmark |
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Standard MuJoCo continuous control benchmarks from [Gymnasium](https://gymnasium.farama.org/) used to evaluate reinforcement learning algorithms. |
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## Benchmark Environments |
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| Environment | Obs Dim | Act Dim | CleanRL SOTA | ParamTatva Best | |
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|---|---|---|---|---| |
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| **Hopper-v5** | 11 | 3 | 2,382 +/- 271 | **3,183.2** (134%) | |
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| **Walker2d-v5** | 17 | 6 | ~4,000 | **4,918.5** (123%) | |
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| **HalfCheetah-v5** | 17 | 6 | ~6,000 | **5,803.9** (97%) | |
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| **Reacher-v5** | 8 | 2 | ~-4 | **-4.2** (~100%) | |
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| **Ant-v5** | 27 | 8 | ~5,000 | 886.6 (training) | |
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| **Humanoid-v5** | 348 | 17 | ~5,000 | 573.8 (training) | |
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## How to Evaluate |
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```bash |
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pip install torch gymnasium[mujoco] |
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``` |
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```python |
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import torch |
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import gymnasium as gym |
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# Load checkpoint |
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checkpoint = torch.load("hopper_v5_sota.pt") |
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agent = Agent(obs_dim=11, act_dim=3) |
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agent.load_state_dict(checkpoint["model_state_dict"]) |
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# Evaluate |
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env = gym.make("Hopper-v5") |
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returns = [] |
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for ep in range(100): |
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obs, _ = env.reset() |
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total = 0 |
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done = False |
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while not done: |
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action = agent.get_action(torch.FloatTensor(obs)) |
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obs, reward, term, trunc, _ = env.step(action.detach().numpy()) |
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total += reward |
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done = term or trunc |
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returns.append(total) |
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print(f"Mean: {sum(returns)/len(returns):.1f}") |
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``` |
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## Reference |
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- Model: [ParamTatva/sanskrit-ppo-hopper-v5](https://huggingface.co/ParamTatva/sanskrit-ppo-hopper-v5) |
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- Blog: [The 371 Wall](https://huggingface.co/ParamTatva/sanskrit-ppo-hopper-v5/blob/main/blog/MultiTask_Policy_Bottleneck.md) |
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- CleanRL Baselines: [vwxyzjn/cleanrl](https://github.com/vwxyzjn/cleanrl) |
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## License |
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Apache 2.0 |
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ParamTatva.org 2026 |
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