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Fix for when not using teacher helper (#379) 

* Fix for when not using teacher helper
* Rename expert to teacher throughout
/develop-generalizationTraining-TrainerController
GitHub 6 年前
当前提交
e0d5b1b0
共有 8 个文件被更改，包括 247 次插入 和 232 次删除
  1. 2
      python/trainer_config.yaml
  2. 4
      python/unitytrainers/bc/models.py
  3. 65
      python/unitytrainers/bc/trainer.py
  4. 2
      python/unitytrainers/ppo/models.py
  5. 306
      unity-environment/Assets/ML-Agents/Examples/Banana/BananaImitation.unity
  6. 54
      unity-environment/Assets/ML-Agents/Scripts/BCTeacherHelper.cs
  7. 46
      unity-environment/Assets/ML-Agents/Scripts/TeacherHelper.cs
  8. 0
      /unity-environment/Assets/ML-Agents/Scripts/BCTeacherHelper.cs.meta

2
python/trainer_config.yaml
查看文件


trainer: imitation
max_steps: 10000
summary_freq: 1000
brain_to_imitate: ExpertBrain
brain_to_imitate: TeacherBrain
batch_size: 16
batches_per_epoch: 5
num_layers: 4

4
python/unitytrainers/bc/models.py
查看文件


if brain.vector_action_space_type == "discrete":
self.action_probs = tf.nn.softmax(self.policy)
self.sample_action = tf.cast(tf.multinomial(self.policy, 1, name="action"), tf.int32)
self.true_action = tf.placeholder(shape=[None], dtype=tf.int32, name="expert_action")
self.true_action = tf.placeholder(shape=[None], dtype=tf.int32, name="teacher_action")
self.action_oh = tf.one_hot(self.true_action, self.a_size)
self.loss = tf.reduce_sum(-tf.log(self.action_probs + 1e-10) * self.action_oh)
self.action_percent = tf.reduce_mean(tf.cast(

self.true_action = tf.placeholder(shape=[None, self.a_size], dtype=tf.float32, name="expert_action")
self.true_action = tf.placeholder(shape=[None, self.a_size], dtype=tf.float32, name="teacher_action")
self.loss = tf.reduce_sum(tf.squared_difference(self.true_action, self.sample_action))
optimizer = tf.train.AdamOptimizer(learning_rate=lr)

65
python/unitytrainers/bc/trainer.py
查看文件


:param take_action_outputs: The outputs of the take action method.
"""
# Used to collect expert experience into training buffer
info_expert = curr_info[self.brain_to_imitate]
next_info_expert = next_info[self.brain_to_imitate]
for agent_id in info_expert.agents:
self.training_buffer[agent_id].last_brain_info = info_expert
# 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.training_buffer[agent_id].last_brain_info = info_teacher
for agent_id in next_info_expert.agents:
stored_info_expert = self.training_buffer[agent_id].last_brain_info
if stored_info_expert is None:
for agent_id in next_info_teacher.agents:
stored_info_teacher = self.training_buffer[agent_id].last_brain_info
if stored_info_teacher is None:
idx = stored_info_expert.agents.index(agent_id)
next_idx = next_info_expert.agents.index(agent_id)
info_expert_record, info_expert_reset = info_expert.text_observations[idx].lower().split(",")
next_info_expert_record, next_info_expert_reset = next_info_expert.text_observations[idx].\
lower().split(",")
if next_info_expert_reset == "true":
self.training_buffer.reset_update_buffer()
if info_expert_record == "true" and next_info_expert_record == "true":
if not stored_info_expert.local_done[idx]:
idx = stored_info_teacher.agents.index(agent_id)
next_idx = next_info_teacher.agents.index(agent_id)
if info_teacher.text_observations[idx] != "":
info_teacher_record, info_teacher_reset = 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.training_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 enumerate(stored_info_expert.visual_observations):
for i, _ in enumerate(stored_info_teacher.visual_observations):
.append(stored_info_expert.visual_observations[i][idx])
.append(stored_info_teacher.visual_observations[i][idx])
.append(stored_info_expert.vector_observations[idx])
.append(stored_info_teacher.vector_observations[idx])
if stored_info_expert.memories.shape[1] == 0:
stored_info_expert.memories = np.zeros((len(stored_info_expert.agents), self.m_size))
self.training_buffer[agent_id]['memory'].append(stored_info_expert.memories[idx])
self.training_buffer[agent_id]['actions'].append(next_info_expert.previous_vector_actions[next_idx])
if stored_info_teacher.memories.shape[1] == 0:
stored_info_teacher.memories = np.zeros((len(stored_info_teacher.agents), self.m_size))
self.training_buffer[agent_id]['memory'].append(stored_info_teacher.memories[idx])
self.training_buffer[agent_id]['actions'].append(next_info_teacher.
previous_vector_actions[next_idx])
info_student = curr_info[self.brain_name]
next_info_student = next_info[self.brain_name]
for agent_id in info_student.agents:

Processing involves calculating value and advantage targets for model updating step.
:param info: Current AllBrainInfo
"""
info_expert = info[self.brain_to_imitate]
for l in range(len(info_expert.agents)):
if ((info_expert.local_done[l] or
len(self.training_buffer[info_expert.agents[l]]['actions']) > self.trainer_parameters[
info_teacher = info[self.brain_to_imitate]
for l in range(len(info_teacher.agents)):
if ((info_teacher.local_done[l] or
len(self.training_buffer[info_teacher.agents[l]]['actions']) > self.trainer_parameters[
and len(self.training_buffer[info_expert.agents[l]]['actions']) > 0):
agent_id = info_expert.agents[l]
and len(self.training_buffer[info_teacher.agents[l]]['actions']) > 0):
agent_id = info_teacher.agents[l]
self.training_buffer.append_update_buffer(agent_id, batch_size=None,
training_length=self.sequence_length)
self.training_buffer[agent_id].reset_agent()

2
python/unitytrainers/ppo/models.py
查看文件


appropriate PPO agent model for the environment.
:param brain: BrainInfo used to generate specific network graph.
:param lr: Learning rate.
:param h_size: Size of hidden layers/
:param h_size: Size of hidden layers
:param epsilon: Value for policy-divergence threshold.
:param beta: Strength of entropy regularization.
:return: a sub-class of PPOAgent tailored to the environment.

306
unity-environment/Assets/ML-Agents/Examples/Banana/BananaImitation.unity
查看文件


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54
unity-environment/Assets/ML-Agents/Scripts/BCTeacherHelper.cs
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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
/// <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 recordExperiences;
bool resetBuffer;
Agent myAgent;
float bufferResetTime;
public KeyCode recordKey = KeyCode.R;
public KeyCode resetKey = KeyCode.C;
// Use this for initialization
void Start () {
recordExperiences = true;
resetBuffer = false;
myAgent = GetComponent<Agent>();
bufferResetTime = Time.time;
}
// Update is called once per frame
void Update () {
if (Input.GetKeyDown(recordKey))
{
recordExperiences = !recordExperiences;
}
if (Input.GetKeyDown(resetKey))
{
resetBuffer = true;
bufferResetTime = Time.time;
}
else
{
resetBuffer = false;
}
Monitor.Log("Recording experiences " + recordKey.ToString(), recordExperiences.ToString());
float timeSinceBufferReset = Time.time - bufferResetTime;
Monitor.Log("Seconds since buffer reset " + resetKey.ToString(), Mathf.FloorToInt(timeSinceBufferReset));
}
void FixedUpdate()
{
// Convert both bools into single comma separated string. Python makes
// assumption that this structure is preserved.
myAgent.SetTextObs(recordExperiences.ToString() + "," + resetBuffer.ToString());
}
}

46
unity-environment/Assets/ML-Agents/Scripts/TeacherHelper.cs
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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class TeacherHelper : MonoBehaviour {
bool recordExperiences;
bool resetBuffer;
Agent myAgent;
float bufferResetTime;
// Use this for initialization
void Start () {
recordExperiences = true;
resetBuffer = false;
myAgent = GetComponent<Agent>();
bufferResetTime = Time.time;
}
// Update is called once per frame
void Update () {
if (Input.GetKeyDown(KeyCode.R))
{
recordExperiences = !recordExperiences;
}
if (Input.GetKeyDown(KeyCode.C))
{
resetBuffer = true;
bufferResetTime = Time.time;
}
else
{
resetBuffer = false;
}
Monitor.Log("Recording experiences", recordExperiences.ToString());
float timeSinceBufferReset = Time.time - bufferResetTime;
Monitor.Log("Seconds since buffer reset", Mathf.FloorToInt(timeSinceBufferReset));
}
void FixedUpdate()
{
// Convert both bools into single comma separated string. Python makes
// assumption that this structure is preserved.
myAgent.SetTextObs(recordExperiences.ToString() + "," + resetBuffer.ToString());
}
}

/unity-environment/Assets/ML-Agents/Scripts/TeacherHelper.cs.meta → /unity-environment/Assets/ML-Agents/Scripts/BCTeacherHelper.cs.meta
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