Auxiliary Control Neural Networks for Reinforcement Learning
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Solution Overview
Problem
Conventional reinforcement learning systems face inefficiencies in training due to reliance on sparse extrinsic rewards, leading to slow training and generic, unsupervised learning that is not closely matched with long-term agent goals.
Innovation Solution
The system trains auxiliary control neural networks and a reward prediction neural network in conjunction with the action selection policy neural network, using gradients to adjust parameters and improve representation learning, thereby enhancing feedback and aligning with long-term goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reinforcement learning systems use sparse extrinsic rewards for training, then the system structure remains simple, but training speed becomes slow and learning becomes generic
Solution Approach 1:
The patent segments the learning system into multiple independent components: an action selection policy neural network for main task control, and separate auxiliary control neural networks for different auxiliary tasks. Each component can be trained independently using its own rewards (extrinsic for main task, intrinsic for auxiliary tasks), then their outputs are combined. This segmentation enables parallel training with different reward signals, significantly improving training speed without requiring a completely redesigned system architecture.
Solution Approach 2:
The patent introduces auxiliary control neural networks as intermediary components between the environment and the main action selection policy. These auxiliary networks process observations and generate auxiliary control signals that are combined with main task signals. The intermediary auxiliary networks enable the system to learn from intrinsic rewards and transfer this knowledge to improve main task performance, bridging the gap between simple reward structures and complex learning objectives.
2Reliability
If conventional reinforcement learning systems rely on extrinsic rewards, then the reward signal structure remains simple, but the learning is not closely matched with long-term agent goals
Solution Approach 1:
The patent applies local quality by assigning different reward signals to different parts of the system. The action selection policy neural network receives extrinsic rewards for main task performance, while the auxiliary control neural networks receive intrinsic rewards based on their specific auxiliary tasks. Each component optimizes its local objective function, and the combined output achieves alignment with long-term goals through the synergistic interaction of these locally optimized components.
Solution Approach 2:
The auxiliary control neural networks perform preliminary learning actions by training on intrinsic rewards before the main task is fully optimized. This preliminary training on auxiliary tasks helps the system understand long-term goal structures and develop better representations, which then transfer to improve main task performance. The auxiliary networks prepare intermediate representations that facilitate more effective main task learning.
3Productivity
If conventional reinforcement learning systems use generic unsupervised learning, then the learning process remains simple, but performance is insufficient compared to supervised approaches
Solution Approach 1:
The patent implements multi-functionality by designing a unified learning framework where the same auxiliary control neural networks serve multiple purposes: they process observations for the main task, generate auxiliary control signals for intrinsic reward optimization, and provide intermediate representations for transfer learning. This universal approach allows the system to leverage the same computational resources for multiple learning objectives simultaneously, improving overall learning efficiency without proportionally increasing complexity.
Solution Approach 2:
The system implements feedback mechanisms where auxiliary control neural networks receive intrinsic rewards based on their performance on auxiliary tasks, and this feedback is used to update their parameters. The improved representations from these auxiliary networks then feed back into the main action selection policy, creating a feedback loop that continuously refines both auxiliary and main task performance. This feedback mechanism enables the system to learn from auxiliary tasks and apply these lessons to improve main task performance.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward. Training each of the auxiliary control neural networks and the reward prediction neural network comprises adjusting values of the respective auxiliary control parameters, reward prediction parameters, and the action selection policy network parameters.