> For the complete documentation index, see [llms.txt](https://docs.amesa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.amesa.com/reference/sdk-reference/train.md).

# Train

## AMESA Train API Documentation

### Overview

The AMESA Train API provides the training infrastructure for agents using Ray for distributed computing. It handles the complete training lifecycle, including algorithm configuration, resource management, checkpointing, and deployment preparation.

### Trainer API

The `Trainer` class is the main interface for training agents.

#### Basic Usage

```python
from composabl import Trainer
from composabl_core.config.trainer_config import (
    BenchmarkConfig,
    RecordConfig,
    PostProcessingConfig,
)

# Create trainer with configuration
trainer = Trainer({
    "target": {
        "local": {"address": "localhost:1337"}
    },
    "env": {
        "name": "my-environment"
    }
})

# Train agent
trainer.train(agent, train_cycles=100)

# Evaluate performance
results = trainer.postprocess(
        agent,
        postprocess_config=PostProcessingConfig(
            file_path="model_files/",
            record=RecordConfig(
                avi_file_name="output.avi",
                gif_file_name="output.gif",
                max_frames=24 * 5,
            ),
            benchmark=BenchmarkConfig(
                num_episodes_per_scenario=2,
                file_name="benchmark.json",
            ),
        ),
    )

# Package for deployment
deployed_agent = trainer.package(agent)

# Clean up resources
trainer.close()
```

### Training Configuration

#### Complete Configuration Example

```python
config = {
    # License (required)
    "license": "your-license-key",
    
    # Training target (required)
    "target": {
        # See Training Targets section for options
    },
    
    # Environment configuration
    "env": {
        "name": "environment-id",
        "init": {
            "param1": "value1",
            "difficulty": "medium"
        }
    }
}
```

### Training Targets

#### Local Target

Train with a simulator running locally:

```python
config = {
    "target": {
        "local": {
            "address": "localhost:1337"
        }
    }
}
```

#### Docker Target

Train with simulators in Docker containers:

```python
config = {
    "target": {
        "docker": {
            "image": "composabl/sim-reactor:latest"
        }
    }
}
```

### Benchmarking

#### Benchmarking

For every scenario on the top-level skill, this will perform \`num\_episodes\_per\_scenario\` amount of inference episodes.

```python
# Evaluate trained agent
eval_results = trainer.postprocess(
        agent,
        postprocess_config=PostProcessingConfig(
            file_path="model_files/",
            benchmark=BenchmarkConfig(
                num_episodes_per_scenario=2,
                file_name="benchmark.json",
            ),
        ),
    )
```

#### Recording

```python
# Evaluation with recording
eval_results = trainer.postprocess(
        agent,
        postprocess_config=PostProcessingConfig(
            file_path="model_files/",
            record=RecordConfig(
                avi_file_name="output.avi",
                gif_file_name="output.gif",
                max_frames=24 * 5,
            ),
        ),
    )
```


---

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