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Feature Deprecation : Online Behavioral Cloning (#2659) 

* Feature Deprecation : Online Behavioral Cloning

In this PR :
 - Delete the online_bc_trainer
 - Delete the tests for online bc
 - delete the configuration file for online bc training

* Deleting the BCTeacherHelper.cs Script
   TODO :
 - Remove usages in the scene
 - Documentation Edits

*DO NOT MERGE*

* IMPORTANT : REMOVED ALL IL SCENES
 - Removed all the IL scenes from the Examples folder

* Removed all mentions of online BC training in the Documentation

* Made a note in the Migrating.md doc about the removal of the Online BC feature.
/develop-gpu-test
GitHub 5 年前
当前提交
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共有 26 个文件被更改，包括 13 次插入 和 5116 次删除
  1. 3
      docs/Learning-Environment-Design-Agents.md
  2. 4
      docs/Learning-Environment-Design-Player-Brains.md
  3. 7
      docs/Learning-Environment-Examples.md
  4. 4
      docs/ML-Agents-Overview.md
  5. 1
      docs/Migrating.md
  6. 60
      docs/Training-Behavioral-Cloning.md
  7. 5
      docs/Training-Imitation-Learning.md
  8. 9
      docs/Training-ML-Agents.md
  9. 76
      ml-agents/mlagents/trainers/tests/test_trainer_util.py
  10. 10
      ml-agents/mlagents/trainers/trainer_util.py
  11. 7
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  13. 880
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  14. 9
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  15. 7
      UnitySDK/Assets/ML-Agents/Examples/Hallway/Scenes/HallwayIL.unity.meta
  16. 653
      UnitySDK/Assets/ML-Agents/Examples/Hallway/Scenes/HallwayIL.unity
  17. 7
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockIL.unity.meta
  18. 714
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockIL.unity
  19. 7
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  20. 566
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  21. 7
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  22. 763
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scenes/TennisIL.unity
  23. 59
      UnitySDK/Assets/ML-Agents/Scripts/BCTeacherHelper.cs
  24. 11
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  25. 110
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  26. 149
      ml-agents/mlagents/trainers/bc/online_trainer.py

3
docs/Learning-Environment-Design-Agents.md
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action, for example, move the agent in one direction or another. In order to
[train an agent using reinforcement learning](Learning-Environment-Design.md),
your agent must calculate a reward value at each action. The reward is used to
discover the optimal decision-making policy. (A reward is not used by already
trained agents or for imitation learning.)
discover the optimal decision-making policy.
The Brain class abstracts out the decision making logic from the Agent itself so
that you can use the same Brain in multiple Agents. How a Brain makes its

4
docs/Learning-Environment-Design-Player-Brains.md
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# Player Brain
The **Player Brain** allows you to control an Agent using keyboard
commands. You can use Player Brains to control a "teacher" Agent that trains
other Agents during [imitation learning](Training-Imitation-Learning.md). You
commands. You can use Player Brains to record demonstrations in order to train
other Agents with [imitation learning](Training-Imitation-Learning.md). You
can also use Player Brains to test your Agents and environment before replacing them by **Learning Brains** and running the training process.
## Player Brain properties

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docs/Learning-Environment-Examples.md
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# Example Learning Environments
# Example Learning Environments
The Unity ML-Agents toolkit contains an expanding set of example environments
which demonstrate various features of the platform. Environments are located in

* Recommended Minimum: 0.2
* Recommended Maximum: 5
* Benchmark Mean Reward: 2.5
* Optional Imitation Learning scene: `TennisIL`.
## [Push Block](https://youtu.be/jKdw216ZgoE)

* Recommended Minimum: 0
* Recommended Maximum: 2000
* Benchmark Mean Reward: 4.5
* Optional Imitation Learning scene: `PushBlockIL`.
## [Wall Jump](https://youtu.be/NITLug2DIWQ)

* Recommended Minimum: 0.5
* Recommended Maximum: 5
* Benchmark Mean Reward: 10
* Optional Imitation Learning scene: `FoodCollectorIL`.
## [Hallway](https://youtu.be/53GyfpPQRUQ)

* Reset Parameters: None
* Benchmark Mean Reward: 0.7
* To speed up training, you can enable curiosity by adding `use_curiosity: true` in `config/trainer_config.yaml`
* Optional Imitation Learning scene: `HallwayIL`.
## [Bouncer](https://youtu.be/Tkv-c-b1b2I)

this environment does not train with the provided default
training parameters.__
* Reset Parameters: None
* Optional Imitation Learning scene: `PyramidsIL`.
* Benchmark Mean Reward: 1.75

4
docs/ML-Agents-Overview.md
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this mode allows providing real examples from a game controller on how the medic
should behave. More specifically, in this mode, the Brain type during training
is set to Player and all the actions performed with the controller (in addition
to the agent observations) will be recorded and sent to the Python API. The
to the agent observations) will be recorded. The
to help speed up reward-based training (RL). We include two algorithms called
to help speed up reward-based training (RL). We include two algorithms called
Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). The
[Training with Imitation Learning](Training-Imitation-Learning.md) tutorial covers these
features in more depth.

1
docs/Migrating.md
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### Important Changes
* The definition of the gRPC service has changed.
* The online BC training feature has been removed.
#### Steps to Migrate
* In order to be able to train, make sure both your ML-Agents Python package and UnitySDK code come from the v0.11 release. Training will not work, for example, if you update the ML-Agents Python package, and only update the API Version in UnitySDK.

60
docs/Training-Behavioral-Cloning.md
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This will use the demonstration file to train a neural network driven agent
to directly imitate the actions provided in the demonstration. The environment
will launch and be used for evaluating the agent's performance during training.
## Online Training
It is also possible to provide demonstrations in realtime during training,
without pre-recording a demonstration file. The steps to do this are as follows:
1. First create two Brains, one which will be the "Teacher," and the other which
will be the "Student." We will assume that the names of the Brain
Assets are "Teacher" and "Student" respectively.
2. The "Teacher" Brain must be a **Player Brain**. You must properly
configure the inputs to map to the corresponding actions.
3. The "Student" Brain must be a **Learning Brain**.
4. The Brain Parameters of both the "Teacher" and "Student" Brains must be
compatible with the agent.
5. Drag both the "Teacher" and "Student" Brain into the Academy's `Broadcast Hub`
and check the `Control` checkbox on the "Student" Brain.
6. Link the Brains to the desired Agents (one Agent as the teacher and at least
one Agent as a student).
7. In `config/online_bc_config.yaml`, add an entry for the "Student" Brain. Set
the `trainer` parameter of this entry to `online_bc`, and the
`brain_to_imitate` parameter to the name of the teacher Brain: "Teacher".
Additionally, set `batches_per_epoch`, which controls how much training to do
each moment. Increase the `max_steps` option if you'd like to keep training
the Agents for a longer period of time.
8. Launch the training process with `mlagents-learn config/online_bc_config.yaml
--train --slow`, and press the :arrow_forward: button in Unity when the
message _"Start training by pressing the Play button in the Unity Editor"_ is
displayed on the screen
9. From the Unity window, control the Agent with the Teacher Brain by providing
"teacher demonstrations" of the behavior you would like to see.
10. Watch as the Agent(s) with the student Brain attached begin to behave
similarly to the demonstrations.
11. Once the Student Agents are exhibiting the desired behavior, end the training
process with `CTL+C` from the command line.
12. Move the resulting `*.nn` file into the `TFModels` subdirectory of the
Assets folder (or a subdirectory within Assets of your choosing) , and use
with `Learning` Brain.
**BC Teacher Helper**
We provide a convenience utility, `BC Teacher Helper` component that you can add
to the Teacher Agent.
<p align="center">
<img src="images/bc_teacher_helper.png"
alt="BC Teacher Helper"
width="375" border="10" />
</p>
This utility enables you to use keyboard shortcuts to do the following:
1. To start and stop recording experiences. This is useful in case you'd like to
interact with the game _but not have the agents learn from these
interactions_. The default command to toggle this is to press `R` on the
keyboard.
2. Reset the training buffer. This enables you to instruct the agents to forget
their buffer of recent experiences. This is useful if you'd like to get them
to quickly learn a new behavior. The default command to reset the buffer is
to press `C` on the keyboard.

5
docs/Training-Imitation-Learning.md
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on the PPO trainer, in addition to using a small GAIL reward signal.
* To train an agent to exactly mimic demonstrations, you can use the
[Behavioral Cloning](Training-Behavioral-Cloning.md) trainer. Behavioral Cloning can be
used offline and online (in-editor), and learns very quickly. However, it usually is ineffective
used with demonstrations (in-editor), and learns very quickly. However, it usually is ineffective
on more complex environments without a large number of demonstrations.
### How to Choose

if you have few (<10) episodes of demonstrations. An example of this is provided for the Crawler example
environment under `CrawlerStaticLearning` in `config/gail_config.yaml`.
If you have plenty of demonstrations and/or a very simple environment, Behavioral Cloning
(online and offline) can be effective and quick. However, it cannot be combined with RL.
If you have plenty of demonstrations and/or a very simple environment, Offline Behavioral Cloning can be effective and quick. However, it cannot be combined with RL.
## Recording Demonstrations

9
docs/Training-ML-Agents.md
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### Training Config File
The training config files `config/trainer_config.yaml`, `config/sac_trainer_config.yaml`,
`config/gail_config.yaml`, `config/online_bc_config.yaml` and `config/offline_bc_config.yaml`
`config/gail_config.yaml` and `config/offline_bc_config.yaml`
training with PPO, SAC, GAIL (with PPO), and online and offline BC. These files are divided into sections.
training with PPO, SAC, GAIL (with PPO), and offline BC. These files are divided into sections.
The **default** section defines the default values for all the available
settings. You can also add new sections to override these defaults to train
specific Brains. Name each of these override sections after the GameObject

| batch_size | The number of experiences in each iteration of gradient descent. | PPO, SAC, 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 online imitation learning, the name of the GameObject containing the Brain component to imitate. | (online)BC |
| demo_path | For offline imitation learning, the file path of the recorded demonstration file | (offline)BC |
| buffer_size | The number of experiences to collect before updating the policy model. In SAC, the max size of the experience buffer. | PPO, SAC |
| buffer_init_steps | The number of experiences to collect into the buffer before updating the policy model. | SAC |

| 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, SAC, BC |
| summary_freq | How often, in steps, to save training statistics. This determines the number of data points shown by TensorBoard. | PPO, SAC, BC |
| tau | How aggressively to update the target network used for bootstrapping value estimation in SAC. | SAC |
| time_horizon | How many steps of experience to collect per-agent before adding it to the experience buffer. | PPO, SAC, (online)BC |
| trainer | The type of training to perform: "ppo", "sac", "offline_bc" or "online_bc". | PPO, SAC, BC |
| time_horizon | How many steps of experience to collect per-agent before adding it to the experience buffer. | PPO, SAC |
| trainer | The type of training to perform: "ppo", "sac" or "offline_bc". | PPO, SAC, BC |
| train_interval | How often to update the agent. | SAC |
| num_update | Number of mini-batches to update the agent with during each update. | SAC |
| use_recurrent | Train using a recurrent neural network. See [Using Recurrent Neural Networks](Feature-Memory.md). | PPO, SAC, BC |

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ml-agents/mlagents/trainers/tests/test_trainer_util.py
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from mlagents.trainers.trainer_metrics import TrainerMetrics
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer
from mlagents.trainers.bc.online_trainer import OnlineBCTrainer
from mlagents.envs.exception import UnityEnvironmentException

"""
default:
trainer: ppo
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
use_curiosity: false
curiosity_strength: 0.0
curiosity_enc_size: 1
"""
)
@pytest.fixture
def dummy_online_bc_config():
return yaml.safe_load(
"""
default:
trainer: online_bc
brain_to_imitate: ExpertBrain
batches_per_epoch: 16
batch_size: 32
beta: 5.0e-3
buffer_size: 512

)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], OfflineBCTrainer)
@patch("mlagents.envs.brain.BrainParameters")
def test_initialize_online_bc_trainer(BrainParametersMock):
summaries_dir = "test_dir"
run_id = "testrun"
model_path = "model_dir"
keep_checkpoints = 1
train_model = True
load_model = False
seed = 11
base_config = dummy_online_bc_config()
expected_config = base_config["default"]
expected_config["summary_path"] = summaries_dir + f"/{run_id}_testbrain"
expected_config["model_path"] = model_path + "/testbrain"
expected_config["keep_checkpoints"] = keep_checkpoints
brain_params_mock = BrainParametersMock()
external_brains = {"testbrain": brain_params_mock}
def mock_constructor(self, brain, trainer_parameters, training, load, seed, run_id):
assert brain == brain_params_mock
assert trainer_parameters == expected_config
assert training == train_model
assert load == load_model
assert seed == seed
assert run_id == run_id
with patch.object(OnlineBCTrainer, "__init__", mock_constructor):
trainers = trainer_util.initialize_trainers(
trainer_config=base_config,
external_brains=external_brains,
summaries_dir=summaries_dir,
run_id=run_id,
model_path=model_path,
keep_checkpoints=keep_checkpoints,
train_model=train_model,
load_model=load_model,
seed=seed,
)
assert "testbrain" in trainers
assert isinstance(trainers["testbrain"], OnlineBCTrainer)
@patch("mlagents.envs.brain.BrainParameters")

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ml-agents/mlagents/trainers/trainer_util.py
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from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.sac.trainer import SACTrainer
from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer
from mlagents.trainers.bc.online_trainer import OnlineBCTrainer
def initialize_trainers(

for brain_name in external_brains:
if trainer_parameters_dict[brain_name]["trainer"] == "offline_bc":
trainers[brain_name] = OfflineBCTrainer(
external_brains[brain_name],
trainer_parameters_dict[brain_name],
train_model,
load_model,
seed,
run_id,
)
elif trainer_parameters_dict[brain_name]["trainer"] == "online_bc":
trainers[brain_name] = OnlineBCTrainer(
external_brains[brain_name],
trainer_parameters_dict[brain_name],
train_model,

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UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockIL.unity.meta
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UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockIL.unity
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59
UnitySDK/Assets/ML-Agents/Scripts/BCTeacherHelper.cs
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using UnityEngine;
namespace MLAgents
{
/// <summary>
/// Behavioral Cloning Helper script. Attach to teacher agent to enable
/// resetting the experience buffer, as well as toggling session recording.
/// </summary>
public class BcTeacherHelper : MonoBehaviour
{
bool m_RecordExperiences;
bool m_ResetBuffer;
Agent m_MyAgent;
float m_BufferResetTime;
public KeyCode recordKey = KeyCode.R;
public KeyCode resetKey = KeyCode.C;
// Use this for initialization
void Start()
{
m_RecordExperiences = true;
m_ResetBuffer = false;
m_MyAgent = GetComponent<Agent>();
m_BufferResetTime = Time.time;
}
// Update is called once per frame
void Update()
{
if (Input.GetKeyDown(recordKey))
{
m_RecordExperiences = !m_RecordExperiences;
}
if (Input.GetKeyDown(resetKey))
{
m_ResetBuffer = true;
m_BufferResetTime = Time.time;
}
else
{
m_ResetBuffer = false;
}
Monitor.Log("Recording experiences " + recordKey, m_RecordExperiences.ToString());
var timeSinceBufferReset = Time.time - m_BufferResetTime;
Monitor.Log("Seconds since buffer reset " + resetKey,
Mathf.FloorToInt(timeSinceBufferReset).ToString());
}
void FixedUpdate()
{
// Convert both bools into single comma separated string. Python makes
// assumption that this structure is preserved.
m_MyAgent.SetTextObs(m_RecordExperiences + "," + m_ResetBuffer);
}
}
}

11
UnitySDK/Assets/ML-Agents/Scripts/BCTeacherHelper.cs.meta
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110
config/online_bc_config.yaml
查看文件


default:
trainer: online_bc
brain_to_imitate: <Your_Brain_Asset_Name>
batch_size: 64
time_horizon: 64
summary_freq: 1000
max_steps: 5.0e4
batches_per_epoch: 10
use_recurrent: false
hidden_units: 128
learning_rate: 3.0e-4
num_layers: 2
sequence_length: 32
memory_size: 256
FoodCollectorLearning:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: FoodCollectorPlayer
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: false
sequence_length: 16
BouncerLearning:
trainer: online_bc
max_steps: 10000
summary_freq: 10
brain_to_imitate: BouncerPlayer
batch_size: 16
batches_per_epoch: 1
num_layers: 1
hidden_units: 64
use_recurrent: false
sequence_length: 16
HallwayLearning:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: HallwayPlayer
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: false
sequence_length: 16
PushBlockLearning:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: PushBlockPlayer
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: false
sequence_length: 16
PyramidsLearning:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: PyramidsPlayer
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: false
sequence_length: 16
TennisLearning:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: TennisPlayer
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: false
sequence_length: 16
StudentBrain:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: TeacherBrain
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: false
sequence_length: 16
StudentRecurrentBrain:
trainer: online_bc
max_steps: 10000
summary_freq: 1000
brain_to_imitate: TeacherBrain
batch_size: 16
batches_per_epoch: 5
num_layers: 4
hidden_units: 64
use_recurrent: true
sequence_length: 32

149
ml-agents/mlagents/trainers/bc/online_trainer.py
查看文件


# # Unity ML-Agents Toolkit
# ## ML-Agent Learning (Behavioral Cloning)
# Contains an implementation of Behavioral Cloning Algorithm
import logging
import numpy as np
from mlagents.envs.brain import AllBrainInfo
from mlagents.envs.action_info import ActionInfoOutputs
from mlagents.trainers.bc.trainer import BCTrainer
logger = logging.getLogger("mlagents.trainers")
class OnlineBCTrainer(BCTrainer):
"""The OnlineBCTrainer is an implementation of Online Behavioral Cloning."""
def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
"""
Responsible for collecting experiences and training PPO model.
:param trainer_parameters: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
:param load: Whether the model should be loaded.
:param seed: The seed the model will be initialized with
:param run_id: The identifier of the current run
"""
super(OnlineBCTrainer, self).__init__(
brain, trainer_parameters, training, load, seed, run_id
)
self.param_keys = [
"brain_to_imitate",
"batch_size",
"time_horizon",
"summary_freq",
"max_steps",
"batches_per_epoch",
"use_recurrent",
"hidden_units",
"learning_rate",
"num_layers",
"sequence_length",
"memory_size",
"model_path",
]
self.check_param_keys()
self.brain_to_imitate = trainer_parameters["brain_to_imitate"]
self.batches_per_epoch = trainer_parameters["batches_per_epoch"]
self.n_sequences = max(
int(trainer_parameters["batch_size"] / self.policy.sequence_length), 1
)
def add_experiences(
self,
curr_info: AllBrainInfo,
next_info: AllBrainInfo,
take_action_outputs: ActionInfoOutputs,
) -> None:
"""
Adds experiences to each agent's experience history.
:param curr_info: Current AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
:param next_info: Next AllBrainInfo (Dictionary of all current brains and corresponding BrainInfo).
:param take_action_outputs: The outputs of the take action method.
"""
# Used to collect teacher experience into training buffer
info_teacher = curr_info[self.brain_to_imitate]
next_info_teacher = next_info[self.brain_to_imitate]
for agent_id in info_teacher.agents:
self.demonstration_buffer[agent_id].last_brain_info = info_teacher
for agent_id in next_info_teacher.agents:
stored_info_teacher = self.demonstration_buffer[agent_id].last_brain_info
if stored_info_teacher is None:
continue
else:
idx = stored_info_teacher.agents.index(agent_id)
next_idx = next_info_teacher.agents.index(agent_id)
if stored_info_teacher.text_observations[idx] != "":
info_teacher_record, info_teacher_reset = (
stored_info_teacher.text_observations[idx].lower().split(",")
)
next_info_teacher_record, next_info_teacher_reset = (
next_info_teacher.text_observations[idx].lower().split(",")
)
if next_info_teacher_reset == "true":
self.demonstration_buffer.reset_update_buffer()
else:
info_teacher_record, next_info_teacher_record = "true", "true"
if info_teacher_record == "true" and next_info_teacher_record == "true":
if not stored_info_teacher.local_done[idx]:
for i in range(self.policy.vis_obs_size):
self.demonstration_buffer[agent_id][
"visual_obs%d" % i
].append(stored_info_teacher.visual_observations[i][idx])
if self.policy.use_vec_obs:
self.demonstration_buffer[agent_id]["vector_obs"].append(
stored_info_teacher.vector_observations[idx]
)
if self.policy.use_recurrent:
if stored_info_teacher.memories.shape[1] == 0:
stored_info_teacher.memories = np.zeros(
(
len(stored_info_teacher.agents),
self.policy.m_size,
)
)
self.demonstration_buffer[agent_id]["memory"].append(
stored_info_teacher.memories[idx]
)
self.demonstration_buffer[agent_id]["actions"].append(
next_info_teacher.previous_vector_actions[next_idx]
)
super(OnlineBCTrainer, self).add_experiences(
curr_info, next_info, take_action_outputs
)
def process_experiences(
self, current_info: AllBrainInfo, next_info: AllBrainInfo
) -> None:
"""
Checks agent histories for processing condition, and processes them as necessary.
Processing involves calculating value and advantage targets for model updating step.
:param current_info: Current AllBrainInfo
:param next_info: Next AllBrainInfo
"""
info_teacher = next_info[self.brain_to_imitate]
for l in range(len(info_teacher.agents)):
teacher_action_list = len(
self.demonstration_buffer[info_teacher.agents[l]]["actions"]
)
horizon_reached = (
teacher_action_list > self.trainer_parameters["time_horizon"]
)
teacher_filled = (
len(self.demonstration_buffer[info_teacher.agents[l]]["actions"]) > 0
)
if (info_teacher.local_done[l] or horizon_reached) and teacher_filled:
agent_id = info_teacher.agents[l]
self.demonstration_buffer.append_update_buffer(
agent_id,
batch_size=None,
training_length=self.policy.sequence_length,
)
self.demonstration_buffer[agent_id].reset_agent()
super(OnlineBCTrainer, self).process_experiences(current_info, next_info)
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