Reinforcement Learning
Keras
English
tensoraerospace
control
ihdp
aerospace
f16
gymnasium
tensorflow
Eval Results (legacy)
Instructions to use TensorAeroSpace/ihdp-f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use TensorAeroSpace/ihdp-f16 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://TensorAeroSpace/ihdp-f16") - Notebooks
- Google Colab
- Kaggle
| {"env": {"name": null, "params": {}}, "policy": {"name": "tensoraerospace.agent.ihdp.model.IHDPAgent", "params": {"actor_settings": {"start_training": 5, "layers": [25, 1], "activations": ["tanh", "tanh"], "learning_rate": 2, "learning_rate_exponent_limit": 10, "type_PE": "combined", "amplitude_3211": 15, "pulse_length_3211": 500.0, "maximum_input": 25, "maximum_q_rate": 20, "WB_limits": 30, "NN_initial": 120, "cascade_actor": false, "learning_rate_cascaded": 1.2}, "critic_settings": {"Q_weights": [8.0], "start_training": -1, "gamma": 0.99, "learning_rate": 15, "learning_rate_exponent_limit": 10, "layers": [25, 1], "activations": ["tanh", "linear"], "WB_limits": 30, "NN_initial": 120, "indices_tracking_states": [1]}, "incremental_settings": {"number_time_steps": 2002, "dt": 0.01, "input_magnitude_limits": 25, "input_rate_limits": 60}, "io": {"tracking_states": ["alpha"], "selected_states": ["theta", "alpha", "q"], "selected_input": ["ele"], "indices_tracking_states": [1], "number_time_steps": 2002}}}} |