LOKI-G is a machine learning project that is a general adaptation of the Locally Optimal search after K-step Imitation (LOKI) algorithm. This project leverages both imitation learning (IL) and reinforcement learning (RL) to train a model. The model starts by learning from demonstration data through IL, then switches to RL for further training. The switching point can either be a fixed iteration or randomly selected based on a user-defined parameter.
LOKI-G uses Closed-form Continuous-Time Neural Networks, and Neural Circuit Policies. These resources are available in the ncps package.
This project requires Python 3.7 or later. The dependencies can be installed with:
pip install -r requirements.txtFirst, adjust the parameters as needed in the run_LOKI-G.sh file. Then, you can start the training process with:
bash run_LOKI-G.sh--env_file: Path to the Python file that defines the environment. This file must be updated to reflect the specific hardware being used and the task being performed.--demonstration_path: Directory containing the demonstration data (default: "./data").--num_outputs: Number of actions the model can output (default: 6).--hard_switch_iter: Iteration to switch from IL to RL during training (default: 18).--random_sample_switch_iter: If set, randomly choose the iteration to switch from IL to RL, with a distribution parameterized byhard_switch_iter(default: True).--il_epochs: Number of epochs to train the imitation learning model (default: 10).--rl_epochs: Number of epochs to train the reinforcement learning model (default: 10).--render: If set, render the environment during training.
The trained model is saved in the saved_models directory located one level above the directory where the script is run. The model is saved in TensorFlow format.
This project is licensed under the terms of the MIT License.
For any queries, please contact Robin Liebert [robinliebert@onmail.com] or Yuriy Yurchenko [yurchenkoyuriy97@gmail.com].