# Training ML-Agents **Table of Contents** - [Training with mlagents-learn](#training-with-mlagents-learn) - [Starting Training](#starting-training) - [Observing Training](#observing-training) - [Stopping and Resuming Training](#stopping-and-resuming-training) - [Loading an Existing Model](#loading-an-existing-model) - [Training Configurations](#training-configurations) - [Trainer Config File](#trainer-config-file) - [Curriculum Learning](#curriculum-learning) - [Specifying Curricula](#specifying-curricula) - [Training with a Curriculum](#training-with-a-curriculum) - [Environment Parameter Randomization](#environment-parameter-randomization) - [Included Sampler Types](#included-sampler-types) - [Defining a New Sampler Type](#defining-a-new-sampler-type) - [Training with Environment Parameter Randomization](#training-with-environment-parameter-randomization) - [Training Using Concurrent Unity Instances](#training-using-concurrent-unity-instances) For a broad overview of reinforcement learning, imitation learning and all the training scenarios, methods and options within the ML-Agents Toolkit, see [ML-Agents Toolkit Overview](ML-Agents-Overview.md). Once your learning environment has been created and is ready for training, the next step is to initiate a training run. Training in the ML-Agents Toolkit is powered by a dedicated Python package, `mlagents`. This package exposes a command `mlagents-learn` that is the single entry point for all training workflows (e.g. reinforcement leaning, imitation learning, curriculum learning). Its implementation can be found at [ml-agents/mlagents/trainers/learn.py](../ml-agents/mlagents/trainers/learn.py). ## Training with mlagents-learn ### Starting Training `mlagents-learn` is the main training utility provided by the ML-Agents Toolkit. It accepts a number of CLI options in addition to a YAML configuration file that contains all the configurations and hyperparameters to be used during training. The set of configurations and hyperparameters to include in this file depend on the agents in your environment and the specific training method you wish to utilize. Keep in mind that the hyperparameter values can have a big impact on the training performance (i.e. your agent's ability to learn a policy that solves the task). In this page, we will review all the hyperparameters for all training methods and provide guidelines and advice on their values. To view a description of all the CLI options accepted by `mlagents-learn`, use the `--help`: ```sh mlagents-learn --help ``` The basic command for training is: ```sh mlagents-learn --env= --run-id= ``` where - `` is the file path of the trainer configuration yaml. This contains all the hyperparameter values. We offer a detailed guide on the structure of this file and the meaning of the hyperparameters (and advice on how to set them) in the dedicated [Training Configurations](#training-configurations) section below. - ``**(Optional)** is the name (including path) of your [Unity executable](Learning-Environment-Executable.md) containing the agents to be trained. If `` is not passed, the training will happen in the Editor. Press the **Play** button in Unity when the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen. - `` is a unique name you can use to identify the results of your training runs. See the [Getting Started Guide](Getting-Started.md#training-a-new-model-with-reinforcement-learning) for a sample execution of the `mlagents-learn` command. #### Observing Training Regardless of which training methods, configurations or hyperparameters you provide, the training process will always generate three artifacts: 1. Summaries (under the `summaries/` folder): these are training metrics that are updated throughout the training process. They are helpful to monitor your training performance and may help inform how to update your hyperparameter values. See [Using TensorBoard](Using-Tensorboard.md) for more details on how to visualize the training metrics. 1. Models (under the `models/` folder): these contain the model checkpoints that are updated throughout training and the final model file (`.nn`). This final model file is generated once either when training completes or is interrupted. 1. Timers file (also under the `summaries/` folder): this contains aggregated metrics on your training process, including time spent on specific code blocks. See [Profiling in Python](Profiling-Python.md) for more information on the timers generated. These artifacts (except the `.nn` file) are updated throughout the training process and finalized when training completes or is interrupted. #### Stopping and Resuming Training To interrupt training and save the current progress, hit `Ctrl+C` once and wait for the model(s) to be saved out. To resume a previously interrupted or completed training run, use the `--resume` flag and make sure to specify the previously used run ID. If you would like to re-run a previously interrupted or completed training run and re-use the same run ID (in this case, overwriting the previously generated artifacts), then use the `--force` flag. #### Loading an Existing Model You can also use this mode to run inference of an already-trained model in Python by using both the `--resume` and `--inference` flags. Note that if you want to run inference in Unity, you should use the [Unity Inference Engine](Getting-Started.md#running-a-pre-trained-model). Alternatively, you might want to start a new training run but _initialize_ it using an already-trained model. You may want to do this, for instance, if your environment changed and you want a new model, but the old behavior is still better than random. You can do this by specifying `--initialize-from=`, where `` is the old run ID. ## Training Configurations The Unity ML-Agents Toolkit provides a wide range of training scenarios, methods and options. As such, specific training runs may require different training configurations and may generate different artifacts and TensorBoard statistics. This section offers a detailed guide into how to manage the different training set-ups withing the toolkit. More specifically, this section offers a detailed guide on four command-line flags for `mlagents-learn` that control the training configurations: - ``: defines the training hyperparameters for each Behavior in the scene - `--curriculum`: defines the set-up for Curriculum Learning - `--sampler`: defines the set-up for Environment Parameter Randomization - `--num-envs`: number of concurrent Unity instances to use during training Reminder that a detailed description of all command-line options can be found by using the help utility: ```sh mlagents-learn --help ``` It is important to highlight that successfully training a Behavior in the ML-Agents Toolkit involves tuning the training hyperparameters and configuration. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like. We provide sample configuration files for our example environments in the [config/](../config/) directory. The `config/trainer_config.yaml` was used to train the 3D Balance Ball in the [Getting Started](Getting-Started.md) guide. That configuration file uses the PPO trainer, but we also have configuration files for SAC and GAIL. Additionally, the set of configurations you provide depend on the training functionalities you use (see [ML-Agents Toolkit Overview](ML-Agents-Overview.md) for a description of all the training functionalities). Each functionality you add typically has its own training configurations or additional configuration files. For instance: - Use PPO or SAC? - Use Recurrent Neural Networks for adding memory to your agents? - Use the intrinsic curiosity module? - Ignore the environment reward signal? - Pre-train using behavioral cloning? (Assuming you have recorded demonstrations.) - Include the GAIL intrinsic reward signals? (Assuming you have recorded demonstrations.) - Use self-play? (Assuming your environment includes multiple agents.) The answers to the above questions will dictate the configuration files and the parameters within them. The rest of this section breaks down the different configuration files and explains the possible settings for each. ### Trainer Config File We begin with the trainer config file, ``, which includes a set of configurations for each Behavior in your scene. Some of the configurations are required while others are optional. To help us get started, below is a sample file that includes all the possible settings if we're using a PPO trainer with all the possible training functionalities enabled (memory, behavioral cloning, curiosity, GAIL and self-play). You will notice that curriculum and environment parameter randomization settings are not part of this file, but their settings live in different files that we'll cover in subsequent sections. ```yaml BehaviorPPO: trainer: ppo # Trainer configs common to PPO/SAC (excluding reward signals) batch_size: 1024 buffer_size: 10240 hidden_units: 128 learning_rate: 3.0e-4 learning_rate_schedule: linear max_steps: 5.0e5 normalize: false num_layers: 2 time_horizon: 64 vis_encoder_type: simple # PPO-specific configs beta: 5.0e-3 epsilon: 0.2 lambd: 0.95 num_epoch: 3 threaded: true # memory use_recurrent: true sequence_length: 64 memory_size: 256 # behavior cloning behavioral_cloning: demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo strength: 0.5 steps: 150000 batch_size: 512 num_epoch: 3 samples_per_update: 0 init_path: reward_signals: # environment reward extrinsic: strength: 1.0 gamma: 0.99 # curiosity module curiosity: strength: 0.02 gamma: 0.99 encoding_size: 256 learning_rate: 3e-4 # GAIL gail: strength: 0.01 gamma: 0.99 encoding_size: 128 demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo learning_rate: 3e-4 use_actions: false use_vail: false # self-play self_play: window: 10 play_against_latest_model_ratio: 0.5 save_steps: 50000 swap_steps: 50000 team_change: 100000 ``` Here is an equivalent file if we use an SAC trainer instead. Notice that the configurations for the additional functionalities (memory, behavioral cloning, curiosity and self-play) remain unchanged. ```yaml BehaviorSAC: trainer: sac # Trainer configs common to PPO/SAC (excluding reward signals) # same as PPO config # SAC-specific configs (replaces the "PPO-specific configs" section above) buffer_init_steps: 0 tau: 0.005 steps_per_update: 1 train_interval: 1 init_entcoef: 1.0 save_replay_buffer: false # memory # same as PPO config # pre-training using behavior cloning behavioral_cloning: # same as PPO config reward_signals: reward_signal_num_update: 1 # only applies to SAC # environment reward extrinsic: # same as PPO config # curiosity module curiosity: # same as PPO config # GAIL gail: # same as PPO config # self-play self_play: # same as PPO config ``` We now break apart the components of the configuration file and describe what each of these parameters mean and provide guidelines on how to set them. See [Training Configuration File](Training-Configuration-File.md) for a detailed description of all the configurations listed above. ### Curriculum Learning To enable curriculum learning, you need to provide the `--curriculum` CLI option and point to a YAML file that defines the curriculum. Here is one example file: ```yml BehaviorY: measure: progress thresholds: [0.1, 0.3, 0.5] min_lesson_length: 100 signal_smoothing: true parameters: wall_height: [1.5, 2.0, 2.5, 4.0] ``` Each group of Agents under the same `Behavior Name` in an environment can have a corresponding curriculum. These curricula are held in what we call a "metacurriculum". A metacurriculum allows different groups of Agents to follow different curricula within the same environment. #### Specifying Curricula In order to define the curricula, the first step is to decide which parameters of the environment will vary. In the case of the Wall Jump environment, the height of the wall is what varies. Rather than adjusting it by hand, we will create a YAML file which describes the structure of the curricula. Within it, we can specify which points in the training process our wall height will change, either based on the percentage of training steps which have taken place, or what the average reward the agent has received in the recent past is. Below is an example config for the curricula for the Wall Jump environment. ```yaml BigWallJump: measure: progress thresholds: [0.1, 0.3, 0.5] min_lesson_length: 100 signal_smoothing: true parameters: big_wall_min_height: [0.0, 4.0, 6.0, 8.0] big_wall_max_height: [4.0, 7.0, 8.0, 8.0] SmallWallJump: measure: progress thresholds: [0.1, 0.3, 0.5] min_lesson_length: 100 signal_smoothing: true parameters: small_wall_height: [1.5, 2.0, 2.5, 4.0] ``` The curriculum for each Behavior has the following parameters: | **Setting** | **Description** | | :------------------ | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `measure` | What to measure learning progress, and advancement in lessons by.

`reward` uses a measure received reward, while `progress` uses the ratio of steps/max_steps. | | `thresholds` | Points in value of `measure` where lesson should be increased. | | `min_lesson_length` | The minimum number of episodes that should be completed before the lesson can change. If `measure` is set to `reward`, the average cumulative reward of the last `min_lesson_length` episodes will be used to determine if the lesson should change. Must be nonnegative.

**Important**: the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if `min_lesson_length` is `100`, the lesson will increment after the average cumulative reward of the last `100` episodes exceeds the current threshold. The mean reward logged to the console is dictated by the `summary_freq` parameter defined above. | | `signal_smoothing` | Whether to weight the current progress measure by previous values. | | `parameters` | Corresponds to environment parameters to control. Length of each array should be one greater than number of thresholds. | #### Training with a Curriculum Once we have specified our metacurriculum and curricula, we can launch `mlagents-learn` using the `–curriculum` flag to point to the config file for our curricula and PPO will train using Curriculum Learning. For example, to train agents in the Wall Jump environment with curriculum learning, we can run: ```sh mlagents-learn config/trainer_config.yaml --curriculum=config/curricula/wall_jump.yaml --run-id=wall-jump-curriculum ``` We can then keep track of the current lessons and progresses via TensorBoard. **Note**: If you are resuming a training session that uses curriculum, please pass the number of the last-reached lesson using the `--lesson` flag when running `mlagents-learn`. ### Environment Parameter Randomization To enable parameter randomization, you need to provide the `--sampler` CLI option and point to a YAML file that defines the curriculum. Here is one example file: ```yaml resampling-interval: 5000 mass: sampler-type: "uniform" min_value: 0.5 max_value: 10 gravity: sampler-type: "multirange_uniform" intervals: [[7, 10], [15, 20]] scale: sampler-type: "uniform" min_value: 0.75 max_value: 3 ``` Note that `mass`, `gravity` and `scale` are the names of the environment parameters that will be sampled. If a parameter specified in the file doesn't exist in the environment, then this parameter will be ignored. | **Setting** | **Description** | | :--------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `resampling-interval` | Number of steps for the agent to train under a particular environment configuration before resetting the environment with a new sample of `Environment Parameters`. | | `sampler-type` | Type of sampler use for this `Environment Parameter`. This is a string that should exist in the `Sampler Factory` (explained below). | | `sampler-type-sub-arguments` | Specify the sub-arguments depending on the `sampler-type`. In the example above, this would correspond to the `intervals` under the `sampler-type` `multirange_uniform` for the `Environment Parameter` called `gravity`. The key name should match the name of the corresponding argument in the sampler definition (explained) below) | #### Included Sampler Types Below is a list of included `sampler-type` as part of the toolkit. - `uniform` - Uniform sampler - Uniformly samples a single float value between defined endpoints. The sub-arguments for this sampler to specify the interval endpoints are as below. The sampling is done in the range of [`min_value`, `max_value`). - **sub-arguments** - `min_value`, `max_value` - `gaussian` - Gaussian sampler - Samples a single float value from the distribution characterized by the mean and standard deviation. The sub-arguments to specify the Gaussian distribution to use are as below. - **sub-arguments** - `mean`, `st_dev` - `multirange_uniform` - Multirange uniform sampler - Uniformly samples a single float value between the specified intervals. Samples by first performing a weight pick of an interval from the list of intervals (weighted based on interval width) and samples uniformly from the selected interval (half-closed interval, same as the uniform sampler). This sampler can take an arbitrary number of intervals in a list in the following format: [[`interval_1_min`, `interval_1_max`], [`interval_2_min`, `interval_2_max`], ...] - **sub-arguments** - `intervals` The implementation of the samplers can be found at `ml-agents-envs/mlagents_envs/sampler_class.py`. #### Defining a New Sampler Type If you want to define your own sampler type, you must first inherit the _Sampler_ base class (included in the `sampler_class` file) and preserve the interface. Once the class for the required method is specified, it must be registered in the Sampler Factory. This can be done by subscribing to the _register_sampler_ method of the `SamplerFactory`. The command is as follows: `SamplerFactory.register_sampler(*custom_sampler_string_key*, *custom_sampler_object*)` Once the Sampler Factory reflects the new register, the new sampler type can be used for sample any `Environment Parameter`. For example, lets say a new sampler type was implemented as below and we register the `CustomSampler` class with the string `custom-sampler` in the Sampler Factory. ```python class CustomSampler(Sampler): def __init__(self, argA, argB, argC): self.possible_vals = [argA, argB, argC] def sample_all(self): return np.random.choice(self.possible_vals) ``` Now we need to specify the new sampler type in the sampler YAML file. For example, we use this new sampler type for the `Environment Parameter` _mass_. ```yaml mass: sampler-type: "custom-sampler" argB: 1 argA: 2 argC: 3 ``` #### Training with Environment Parameter Randomization After the sampler YAML file is defined, we proceed by launching `mlagents-learn` and specify our configured sampler file with the `--sampler` flag. For example, if we wanted to train the 3D ball agent with parameter randomization using `Environment Parameters` with `config/3dball_randomize.yaml` sampling setup, we would run ```sh mlagents-learn config/trainer_config.yaml --sampler=config/3dball_randomize.yaml --run-id=3D-Ball-randomize ``` We can observe progress and metrics via Tensorboard. ### Training Using Concurrent Unity Instances In order to run concurrent Unity instances during training, set the number of environment instances using the command line option `--num-envs=` when you invoke `mlagents-learn`. Optionally, you can also set the `--base-port`, which is the starting port used for the concurrent Unity instances. Some considerations: - **Buffer Size** - If you are having trouble getting an agent to train, even with multiple concurrent Unity instances, you could increase `buffer_size` in the `config/trainer_config.yaml` file. A common practice is to multiply `buffer_size` by `num-envs`. - **Resource Constraints** - Invoking concurrent Unity instances is constrained by the resources on the machine. Please use discretion when setting `--num-envs=`. - **Result Variation Using Concurrent Unity Instances** - If you keep all the hyperparameters the same, but change `--num-envs=`, the results and model would likely change.