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0d252e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | # OptimusEnv Testing and Verification Guide
This guide provides step-by-step instructions for testing, verifying, and interacting with the **OptimusEnv** reinforcement learning environment.
## 📱 Live Environment
The official OpenEnv-compliant endpoint is hosted at:
**`https://manasdutta04-optimusenv.hf.space/`**
---
## 1. Quick Health Check (CURL)
To verify the environment is online and responsive:
```bash
curl https://manasdutta04-optimusenv.hf.space/health
```
**Expected Output:** `{"status":"ok", "env":"OptimusEnv", "version":"1.0.0"}`
---
## 2. Inspecting the Task Suite
List all available RL tasks, including their descriptions and required action schemas:
```bash
curl https://manasdutta04-optimusenv.hf.space/tasks
```
---
## 3. Running an Episode (Step-by-Step)
You can manually interact with the environment to verify the transition logic:
### Step 1: Start/Reset an Episode
Choose a task (e.g., `task_1`):
```bash
curl -X POST https://manasdutta04-optimusenv.hf.space/reset \
-H "Content-Type: application/json" \
-d '{"task":"task_1"}'
```
### Step 2: Perform an Action
Run one training epoch with specific hyperparameters:
```bash
curl -X POST https://manasdutta04-optimusenv.hf.space/step \
-H "Content-Type: application/json" \
-d '{
"learning_rate": 0.001,
"batch_size": 64,
"optimizer": "adamw",
"num_layers": 2,
"hidden_dim": 128,
"use_amp": false,
"lr_schedule": "cosine",
"weight_decay": 0.0001
}'
```
---
## 4. Comprehensive Testing Scripts
For full execution verification, use the provided Python scripts in the repository.
### A. Run the Heuristic Baseline
This runs a pre-defined policy across all three tasks and reports a final score:
```bash
# Point the script to the live URL
python baseline/run_baseline.py --host https://manasdutta04-optimusenv.hf.space
```
### B. Run the Submission Inference
Simulate an agent run (this uses the same interface as the hackathon validator):
```bash
export API_BASE_URL=https://manasdutta04-optimusenv.hf.space
python inference.py
```
---
## 💡 Troubleshooting and Tips
> [!TIP]
> **Dataset Pre-caching:** The first time a task is run, the environment may take a few seconds to download the relevant PyTorch datasets (MNIST, CIFAR-10, etc.). Subsequent steps/resets will be much faster.
> [!IMPORTANT]
> **Port Consistency:** If running locally, the environment serves on port **8000**. On Hugging Face Spaces, use the root URL provided.
> [!WARNING]
> **Crash Penalty:** Running an action with extremely high learning rates or too many layers (causing OOM) will result in a reward of `-1.0` and terminate the episode immediately.
## 📊 Interpreting Results
- **`/grader`**: This endpoint returns the final normalized score `[0.0 - 1.0]` for the last completed episode.
- **`/baseline`**: This runs a server-side heuristic test to provide a reference score for all tasks.
---
*For more technical details on the reward function and observation space, refer to the [README.md](./README.md).*
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