| **Continuous Player Actions** | | The mapping for the continuous vector action space. Shown when the action space is **Continuous**. |
| | **Size** | The number of key commands defined. You can assign more than one command to the same action index in order to send different values for that action. (If you press both keys at the same time, deterministic results are not guaranteed.) |
| | **Element 0–N** | The mapping of keys to action values. |
| | **Key** | The key on the keyboard. |
| | **Index** | The element of the Agent's action vector to set when this key is pressed. The index value cannot exceed the size of the Action Space (minus 1, since it is an array index). |
| | **Value** | The value to send to the Agent as its action for the specified index when the mapped key is pressed. All other members of the action vector are set to 0. |
| **Discrete Player Actions** | | The mapping for the discrete vector action space. Shown when the action space is **Discrete**. |
| | **Size** | The number of key commands defined. |
| | **Element 0–N** | The mapping of keys to action values. |
| | **Key** | The key on the keyboard. |
| | **Branch Index** | The element of the Agent's action vector to set when this key is pressed. The index value cannot exceed the size of the Action Space (minus 1, since it is an array index). |
| | **Value** | The value to send to the Agent as its action when the mapped key is pressed. Cannot exceed the max value for the associated branch (minus 1, since it is an array index). |
For more information about the Unity input system, see
settings. (This GameObject will be a child of the Academy in your scene.)
Sections for the example environments are included in the provided config file.
| **Setting** | **Description** | **Applies To Trainer\***|
| :-- | :-- | :-- |
| batch_size | The number of experiences in each iteration of gradient descent.| PPO, BC |
| batches_per_epoch | In imitation learning, the number of batches of training examples to collect before training the model.| BC |
| beta | The strength of entropy regularization.| PPO |
| brain\_to\_imitate | For imitation learning, the name of the GameObject containing the Brain component to imitate. | BC |
| buffer_size | The number of experiences to collect before updating the policy model. | PPO |
| curiosity\_enc\_size | The size of the encoding to use in the forward and inverse models in the Curioity module. | PPO |
| curiosity_strength | Magnitude of intrinsic reward generated by Intrinsic Curiosity Module. | PPO |
| epsilon | Influences how rapidly the policy can evolve during training.| PPO |
| gamma | The reward discount rate for the Generalized Advantage Estimator (GAE). | PPO |
| hidden_units | The number of units in the hidden layers of the neural network. | PPO, BC |
| lambd | The regularization parameter. | PPO |
| learning_rate | The initial learning rate for gradient descent. | PPO, BC |
| max_steps | The maximum number of simulation steps to run during a training session. | PPO, BC |
| memory_size | The size of the memory an agent must keep. Used for training with a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md). | PPO, BC |
| num_epoch | The number of passes to make through the experience buffer when performing gradient descent optimization. | PPO |
| num_layers | The number of hidden layers in the neural network. | PPO, BC |
| sequence_length | Defines how long the sequences of experiences must be while training. Only used for training with a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md). | PPO, BC |
| summary_freq | How often, in steps, to save training statistics. This determines the number of data points shown by TensorBoard. | PPO, BC |
| time_horizon | How many steps of experience to collect per-agent before adding it to the experience buffer. | PPO, BC |
| trainer | The type of training to perform: "ppo" or "imitation".| PPO, BC |
| use_curiosity | Train using an additional intrinsic reward signal generated from Intrinsic Curiosity Module. | PPO |
| use_recurrent | Train using a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md).| PPO, BC |
| **Setting**| **Description**| **Applies To Trainer\***|
| batch_size | The number of experiences in each iteration of gradient descent.| PPO, BC |
| batches_per_epoch | In imitation learning, the number of batches of training examples to collect before training the model.| BC |
| beta | The strength of entropy regularization.| PPO |
| brain\_to\_imitate | For imitation learning, the name of the GameObject containing the Brain component to imitate. | BC |
| buffer_size | The number of experiences to collect before updating the policy model. | PPO |
| curiosity\_enc\_size | The size of the encoding to use in the forward and inverse models in the Curioity module. | PPO |
| curiosity_strength | Magnitude of intrinsic reward generated by Intrinsic Curiosity Module. | PPO |
| epsilon | Influences how rapidly the policy can evolve during training.| PPO |
| gamma | The reward discount rate for the Generalized Advantage Estimator (GAE). | PPO |
| hidden_units | The number of units in the hidden layers of the neural network. | PPO, BC |
| lambd | The regularization parameter. | PPO |
| learning_rate | The initial learning rate for gradient descent. | PPO, BC |
| max_steps | The maximum number of simulation steps to run during a training session. | PPO, BC |
| memory_size | The size of the memory an agent must keep. Used for training with a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md). | PPO, BC |
| num_epoch | The number of passes to make through the experience buffer when performing gradient descent optimization. | PPO |
| num_layers | The number of hidden layers in the neural network. | PPO, BC |
| sequence_length | Defines how long the sequences of experiences must be while training. Only used for training with a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md). | PPO, BC |
| summary_freq | How often, in steps, to save training statistics. This determines the number of data points shown by TensorBoard. | PPO, BC |
| time_horizon | How many steps of experience to collect per-agent before adding it to the experience buffer. | PPO, BC |
| trainer | The type of training to perform: "ppo" or "imitation".| PPO, BC |
| use_curiosity | Train using an additional intrinsic reward signal generated from Intrinsic Curiosity Module. | PPO |
| use_recurrent | Train using a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md).| PPO, BC |
\*PPO = Proximal Policy Optimization, BC = Behavioral Cloning (Imitation)
* `mlagents.trainers`: A set of Reinforcement Learning
algorithms designed to be used with Unity environments. Access them using the
`mlagents-learn` access point. See [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Training-ML-Agents.md) for more information on using
this package.
* `mlagents.trainers`: A set of Reinforcement Learning algorithms designed to be
used with Unity environments. Access them using the: `mlagents-learn` access
GitHub
6 年前