Update README.md

- Enhanced environment framework for easier creation of custom environments

- New service architecture for efficient distributed training

- Check out our new guides:

  - [Creating Environments](docs/creating_environments.md): Learn how to build custom environments

  - [Service Architecture](docs/service_architecture.md): Understand our scalable training infrastructure

  - [Creating Environments](docs/create-env.md): New environment protocal.

  - [Creating Services](docs/create-service.md): We now support hosting environments in a seperate process

## Installation

# Then, you can run different environments and algorithms:

#Run a server process in a saperate tmux session if you want to train in env-as-service mode

python vagen/server/server.py

# Frozen Lake Environment

bash vagen/examples/frozen_lake_aico/run.sh         # AICO without service

bash vagen/examples/frozen_lake_aico_service/run.sh # AICO with service

bash vagen/examples/frozen_lake_trico/run.sh        # TRICO without service

bash vagen/examples/frozen_lake_aico_service/run.sh # AICO with service

# SVG Generation

bash vagen/examples/svg_aico/run.sh                 # AICO without service

bash vagen/examples/svg_trico/run.sh                # TRICO without service

```

## How to Add New Environment

## How to Add New Environment and Services

VAGEN supports creating custom environments for agent training, which could simply inherit from `BaseEnv` and `BaseEnvConfig` classes to implement your environment.

For detailed instructions, see our [Creating Environments](docs/create-env.md) guide. You may also want to check our [Creating Service](docs/create-service.md) for scaling your environments.

See our [Creating Environments](./docs/create-env.md) guide. You may also want to check our [Creating Service](./docs/create-service.md) for scaling your environments.

## How to Add New Model

VAGEN supports integration with various language models. To add a new model:

1. Define model interface in the mllm_agent architecture

2. Implement model-specific adapters and handlers

3. Configure the model in your training scripts

For detailed implementation examples, refer to our code architecture documentation based on the [VERL architecture](https://verl.readthedocs.io/en/latest/index.html).

1. Refer to [VERL](https://verl.readthedocs.io/en/latest/index.html) for adding new MLLM.

2. Refer to [QwenVLRolloutManager](./vagen/mllm_agent/rollout.py) to understand how rollout works. In most cases, you can use QwenVLRolloutManager directly with only minor modifications to the model's special tokens

## Experimental Results

> To reproduce our experiment, please refer to document: [Reproduce Experiments](docs/reproduce-exp.md)