Skip to content

Installation and Run Experiments

This guide provides instructions for installing environment and running experiments with VAGEN, a multi-turn reinforcement learning framework for training VLM Agents. VAGEN leverages the TRICO algorithm to efficiently train VLMs for visual agentic tasks.

Installation

Before running experiments, ensure you have set up the environment properly:

# Create a new conda environment
conda create -n vagen python=3.10 -y
conda activate vagen

# Install verl
git clone https://github.com/JamesKrW/verl.git
cd verl
pip install -e .
cd ../

# Install VAGEN
git clone https://github.com/RAGEN-AI/VAGEN.git
cd VAGEN
bash scripts/install.sh

# Login to wandb for experiment tracking
wandb login

Running Experiments

Basic Approach

# Login to wandb
wandb login

# You can run different environments and algorithms:
bash scripts/examples/masked_grpo/frozenlake/grounding_worldmodeling/run_tmux.sh
bash scripts/examples/finegrained/sokoban/grounding_worldmodeling/run_tmux.sh
bash scripts/examples/masked_turn_ppo/frozenlake/grounding_worldmodeling/run_tmux.sh

# Use Visual Reasoning Reward
# Setup OPENAI_API_KEY in the Environment
bash scripts/examples/state_reward_finegrained/sokoban/grounding_worldmodeling/run_tmux.sh

Support Environment

  • FrozenLake: A simple grid-based environment
  • Sokoban: A visual puzzle-solving environment with box pushing
  • SVG: An environment that generate svg code fot provided image. Supports reward model integration
  • Navigation: An environment of visual navigation task for embodied AI
  • Primitive-skill: An environment of primitive skill for embodied AI
  • Blackjack: A simple card game environment

For information on creating new environment, please refer to our "Create your Own Environment" guide.

For information on creating service for training based on your new environment, please refer to our "Create your Own Service guide"