Reinforcement Learning Sandbox Example

Demonstrates running a basic RL training loop (CartPole + DQN) inside an isolated OpenSandbox container. The example installs RL dependencies in the sandbox, trains a policy, saves a checkpoint, and returns a training summary.

Start OpenSandbox server [local]

Start the local OpenSandbox server:

uv pip install opensandbox-server
opensandbox-server init-config ~/.sandbox.toml --example docker
opensandbox-server

Run the Example

# Install OpenSandbox package
uv pip install opensandbox

# Run the example
uv run python examples/rl-training/main.py

The script provisions a sandbox, installs RL dependencies, trains a DQN agent on CartPole, saves a checkpoint, and prints the JSON training summary.

Environment Variables

Variable	Default	Description
SANDBOX_DOMAIN	localhost:8080	Sandbox service address
SANDBOX_API_KEY	(optional)	API key if your server requires authentication
SANDBOX_IMAGE	sandbox-registry.cn-zhangjiakou.cr.aliyuncs.com/opensandbox/code-interpreter:v1.1.0	Docker image to use
RL_TIMESTEPS	5000	Training timesteps to run

TensorBoard

The training script logs to runs/. To visualize metrics, open a shell in the sandbox and run:

tensorboard --logdir runs --host 0.0.0.0 --port 6006

References

• Source code on GitHub