| # Learning Frameworks |
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| ### Overview |
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| iGibson can be used with any learning framework that accommodates OpenAI gym interface. Feel free to use your favorite ones. |
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| ### Examples |
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| #### TF-Agents |
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| In this example, we show an environment wrapper of [TF-Agents](https://github.com/tensorflow/agents) for iGibson and an example training code for [SAC agent](https://arxiv.org/abs/1801.01290). The code can be found in [our fork of TF-Agents](https://github.com/StanfordVL/agents/): [agents/blob/igibson/tf_agents/environments/suite_gibson.py](https://github.com/StanfordVL/agents/blob/igibson/tf_agents/environments/suite_gibson.py) and [agents/blob/igibson/tf_agents/agents/sac/examples/v1/train_single_env.sh](https://github.com/StanfordVL/agents/blob/igibson/tf_agents/agents/sac/examples/v1/train_single_env.sh). |
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| ```python |
| def load(config_file, |
| model_id=None, |
| env_mode='headless', |
| action_timestep=1.0 / 10.0, |
| physics_timestep=1.0 / 40.0, |
| device_idx=0, |
| gym_env_wrappers=(), |
| env_wrappers=(), |
| spec_dtype_map=None): |
| config_file = os.path.join(os.path.dirname(igibson.__file__), config_file) |
| env = iGibsonEnv(config_file=config_file, |
| scene_id=model_id, |
| mode=env_mode, |
| action_timestep=action_timestep, |
| physics_timestep=physics_timestep, |
| device_idx=device_idx) |
| |
| discount = env.config.get('discount_factor', 0.99) |
| max_episode_steps = env.config.get('max_step', 500) |
| |
| return wrap_env( |
| env, |
| discount=discount, |
| max_episode_steps=max_episode_steps, |
| gym_env_wrappers=gym_env_wrappers, |
| time_limit_wrapper=wrappers.TimeLimit, |
| env_wrappers=env_wrappers, |
| spec_dtype_map=spec_dtype_map, |
| auto_reset=True |
| ) |
| ``` |
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