Discussing the article: "Neural networks made easy (Part 41): Hierarchical models"

New comment
 

Check out the new article: Neural networks made easy (Part 41): Hierarchical models.

The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.

The Scheduled Auxiliary Control (SAC-X) algorithm is a reinforcement learning method that uses a hierarchical structure to make decisions. It represents a new approach towards solving problems with sparse rewards. It is based on four main principles:

  1. Each state-action pair is accompanied by a reward vector consisting of (usually sparse) external rewards and (usually sparse) internal auxiliary rewards.
  2. Each reward entry is assigned a policy, called an intent, that learns to maximize the corresponding accumulated reward.
  3. There is a high-level scheduler that selects and executes individual intents with the goal of improving the performance of the external task agent.
  4. Learning occurs outside of policy (asynchronously from policy execution), and experience is exchanged between intentions - for the effective use of information.

The SAC-X algorithm uses these principles to efficiently solve sparse reward problems. Reward vectors allow learning from different aspects of a task and create multiple intentions, each of which maximizes its own reward. The planner manages the execution of intentions by choosing the optimal strategy to achieve external objectives. Learning occurs outside of politics allowing experiences from different intentions to be used for effective learning.

This approach allows the agent to efficiently solve sparse reward problems by learning from external and internal rewards. Using the planner allows coordination of actions. It also involves the exchange of experience between intentions, which promotes the efficient use of information and improves the overall performance of the agent.


SAC-X enables more efficient and flexible agent training in sparse reward environments. A key feature of SAC-X is the use of internal auxiliary rewards, which helps overcome the sparsity problem and facilitate learning on low-reward tasks.

In the SAC-X learning process, each intent has its own policy that maximizes the corresponding auxiliary reward. The scheduler determines which intentions will be selected and executed at any given time. This allows the agent to learn from different aspects of a task and effectively use available information to achieve optimal results.

Author: Dmitriy Gizlyk

New comment