add doc: frozenlake quickstart

cookbook/frozenlake/quickstart.md

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+# FrozenLake Experiment Quick Start Guide
+
+This guide helps you quickly set up and run FrozenLake experiments with ExperienceMaker integration.
+
+## Env Setup
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/modelscope/ExperienceMaker.git
+cd ExperienceMaker/cookbook/frozenlake
+```
+
+### 2. FrozenLake Environment Setup
+
+Install Gymnasium for FrozenLake environment:
+
+```bash
+pip install gymnasium
+```
+
+### 3. Start ExperienceMaker Service
+
+Install ExperienceMaker (if not already installed)
+If you haven't installed the ExperienceMaker environment yet, follow these steps:
+```bash
+# Go back to the project root
+cd ../..
+
+# Create ExperienceMaker environment
+conda create -p ./em-env python==3.12
+conda activate ./em-env
+
+# Install ExperienceMaker
+pip install .
+```
+
+Launch the ExperienceMaker service to enable experience library functionality:
+
+```bash
+experiencemaker \
+  http_service.port=8001 \
+  llm.default.model_name=qwen-max-latest \
+  embedding_model.default.model_name=text-embedding-v4 \
+  vector_store.default.backend=local_file
+```
+
+Load default experience library for FrozenLake:
+```bash
+curl -X POST "http://0.0.0.0:8001/vector_store" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "workspace_id": "frozenlake_no_slippery",
+    "action": "dump",
+    "path": "./experience_library"
+  }'
+```
+Now you have loaded the default FrozenLake experience library to enable experience-based agent!
+
+## Run Experiments
+
+### 1. Quick Test: Performance Evaluation Only (Default)
+
+Run the main experiment script to test agent performance using existing experience:
+
+```bash
+python run_frozenlake.py
+```
+
+**What this does:**
+- Tests the agent on randomly generated FrozenLake maps
+- Uses the default experience library (`frozenlake_no_slippery`)
+- Evaluates performance with multiple runs for statistical significance
+- Results are automatically saved to `./exp_result/` directory
+
+### 2. Advanced: Training + Testing (Experience Generation)
+
+To create new experiences through training and then test performance:
+
+```bash
+python run_frozenlake.py --enable-training
+```
+
+**What this does:**
+- **Stage 1 (Training)**: Generates new experiences by solving training maps
+- **Stage 2 (Testing)**: Evaluates performance using the generated experiences
+- Compares baseline performance vs experience-enhanced performance
+
+### 3. Custom Configuration Examples
+
+**Basic customization:**
+```bash
+python run_frozenlake.py --experiment-name "my_frozenlake_test" --max-workers 8
+```
+
+**Enable slippery mode:**
+```bash
+python run_frozenlake.py --slippery --experiment-name "frozenlake_slippery"
+```
+
+**Full training experiment:**
+```bash
+python run_frozenlake.py \
+    --enable-training \
+    --experiment-name "frozenlake_training_experiment" \
+    --max-workers 8 \
+    --training-runs 4 \
+    --num-training-maps 50 \
+    --test-runs 5 \
+    --num-test-maps 100 \
+    --slippery
+```
+
+**View all available options:**
+```bash
+python run_frozenlake.py --help
+```
+
+### 4. View Experiment Results
+
+After running experiments, analyze the statistical results:
+
+```bash
+python run_exp_statistic.py
+```
+
+**What this script does:**
+- Processes all result files in `./exp_result/`
+- Calculates success rates and performance metrics
+- Generates a summary table showing performance comparisons
+- Saves results to `experiment_summary.csv`
+
+## Configuration Parameters
+
+| Parameter | Default Value | Description |
+|-----------|---------------|-------------|
+| `--experiment-name` | `frozenlake_no_slippery` | Name of the experiment |
+| `--max-workers` | `4` | Number of parallel workers |
+| `--enable-training` | `False` | Enable training phase (experience generation) |
+| `--training-runs` | `4` | Number of runs per training map |
+| `--num-training-maps` | `50` | Number of training maps |
+| `--test-runs` | `1` | Number of runs per test configuration |
+| `--num-test-maps` | `100` | Number of test maps to use |
+| `--slippery` | `False` | Enable slippery ice mode |
+
+## Understanding Results
+
+The experiment evaluates agent performance on FrozenLake maps:
+
+- **Success Rate**: Percentage of episodes that reach the goal
+- **Default Mode**: Uses existing experience library for quick testing
+- **Training Mode**: Generates new experiences then tests performance improvement
+
+**Output Files:**
+- `./exp_result/*.jsonl`: Raw experiment results
+- `./exp_result/experiment_summary.csv`: Statistical summary
+- Console output: Real-time progress and metrics