// var errors = BarracudaModelParamLoader.CheckModel(model, brainParameters, new SensorComponent[] { sensor_21_20_3, sensor_20_22_3 }, new ActuatorComponent[0]);
"description":"Use state-of-the-art machine learning to create intelligent character behaviors in any Unity environment (games, robotics, film, etc.).",
| `init_path` | (default = None) Initialize trainer from a previously saved model. Note that the prior run should have used the same trainer configurations as the current run, and have been saved with the same version of ML-Agents. <br><br>You should provide the full path to the folder where the checkpoints were saved, e.g. `./models/{run-id}/{behavior_name}`. This option is provided in case you want to initialize different behaviors from different runs; in most cases, it is sufficient to use the `--initialize-from` CLI parameter to initialize all models from the same run. |
| `threaded` | (default = `true`) By default, model updates can happen while the environment is being stepped. This violates the [on-policy](https://spinningup.openai.com/en/latest/user/algorithms.html#the-on-policy-algorithms) assumption of PPO slightly in exchange for a training speedup. To maintain the strict on-policyness of PPO, you can disable parallel updates by setting `threaded` to `false`. There is usually no reason to turn `threaded` off for SAC. |
| `hyperparameters -> learning_rate` | (default = `3e-4`) Initial learning rate for gradient descent. Corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase. <br><br>Typical range: `1e-5` - `1e-3` |
| `hyperparameters -> batch_size` | Number of experiences in each iteration of gradient descent. **This should always be multiple times smaller than `buffer_size`**. If you are using a continuous action space, this value should be large (in the order of 1000s). If you are using a discrete action space, this value should be smaller (in order of 10s). <br><br> Typical range: (Continuous - PPO): `512` - `5120`; (Continuous - SAC): `128` - `1024`; (Discrete, PPO & SAC): `32` - `512`. |
| `hyperparameters -> batch_size` | Number of experiences in each iteration of gradient descent. **This should always be multiple times smaller than `buffer_size`**. If you are using continuous actions, this value should be large (on the order of 1000s). If you are using only discrete actions, this value should be smaller (on the order of 10s). <br><br> Typical range: (Continuous - PPO): `512` - `5120`; (Continuous - SAC): `128` - `1024`; (Discrete, PPO & SAC): `32` - `512`. |
| `hyperparameters -> buffer_size` | (default = `10240` for PPO and `50000` for SAC)<br>**PPO:** Number of experiences to collect before updating the policy model. Corresponds to how many experiences should be collected before we do any learning or updating of the model. **This should be multiple times larger than `batch_size`**. Typically a larger `buffer_size` corresponds to more stable training updates. <br>**SAC:** The max size of the experience buffer - on the order of thousands of times longer than your episodes, so that SAC can learn from old as well as new experiences. <br><br>Typical range: PPO: `2048` - `409600`; SAC: `50000` - `1000000` |
| `hyperparameters -> learning_rate_schedule` | (default = `linear` for PPO and `constant` for SAC) Determines how learning rate changes over time. For PPO, we recommend decaying learning rate until max_steps so learning converges more stably. However, for some cases (e.g. training for an unknown amount of time) this feature can be disabled. For SAC, we recommend holding learning rate constant so that the agent can continue to learn until its Q function converges naturally. <br><br>`linear` decays the learning_rate linearly, reaching 0 at max_steps, while `constant` keeps the learning rate constant for the entire training run. |
| `network_settings -> hidden_units` | (default = `128`) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. <br><br> Typical range: `32` - `512` |
A few considerations when deciding to use memory:
- LSTM does not work well with continuous vector actions. Please use
- LSTM does not work well with continuous actions. Please use
discrete actions for better results.
- Since the memories must be sent back and forth between Python and Unity, using
Where `<your_env>` corresponds to the path to your environment executable.
Where `<your_env>` corresponds to the path to your environment executable (i.e. `/home/UserName/Build/yourFile`).
**Note:** When running your environment in headless mode, you must append `--no-graphics` to your mlagents-learn command, as it won't train otherwise.
You can test this simply by aborting a training and check if it says "Model Saved" or "Aborted", or see if it generated the .onnx in the result folder.
vincentpierre
3 年前
