Action-Selection Neural Network Training With Auxiliary Losses
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Solution Overview
Problem
Reinforcement learning systems require large amounts of training data and can perform poorly during training, posing challenges for real-world applications like controlling vehicles or managing data centers, especially when in-situ training is not feasible.
Innovation Solution
A neural network system is trained using supervised learning techniques with expert demonstrations, incorporating auxiliary prediction task neural networks to learn from small data sets and reduce susceptibility to overfitting, utilizing multiple losses and noisy weights to enhance generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning systems are trained using traditional methods, then they can learn complex tasks, but they require large amounts of training data and perform poorly during training
Solution Approach 1:
The system performs preliminary supervised learning from expert demonstrations before reinforcement learning training. This preliminary action pre-trains the neural network with labeled data, establishing a good initial policy that reduces the amount of additional training data needed and improves performance during the subsequent reinforcement learning phase.
Solution Approach 2:
The training process is segmented into distinct phases: supervised learning from demonstrations, then reinforcement learning fine-tuning. Each phase uses different learning objectives and data requirements, allowing the system to efficiently learn from small demonstration sets while adapting to complex environments through structured multi-stage training.
2Manufacturing precision
If the neural network is trained to accurately reproduce demonstrated actions, then it learns the task well, but it becomes susceptible to overfitting on small data sets
Solution Approach 1:
The system uses multiple loss functions that provide different feedback signals during training: supervised loss from demonstrated actions, reinforcement learning rewards, and auxiliary prediction losses. This multi-faceted feedback prevents the network from over-relying on any single signal, improving generalization while maintaining accuracy on demonstrated tasks.
Solution Approach 2:
The training process dynamically adjusts network parameters and loss weights across different training stages. During supervised learning, the network learns accurate action reproduction; during reinforcement learning fine-tuning, parameters are adjusted to maximize rewards while maintaining generalization through exploration, effectively balancing accuracy and adaptability.
3Productivity
If the system uses multiple loss functions and auxiliary prediction tasks, then it learns more efficiently from limited data, but the training process becomes more complex
Solution Approach 1:
The neural network is designed with multi-functionality, serving both as an action selector and as predictors for auxiliary tasks (state prediction, reward prediction, termination prediction). This universal architecture allows a single network to learn multiple representations simultaneously, improving training efficiency from limited data while avoiding the need for separate networks for each function.
Solution Approach 2:
Multiple loss functions are merged into a unified training objective that combines supervised learning losses, reinforcement learning rewards, and auxiliary prediction losses. This combination allows the system to leverage limited demonstration data more effectively by extracting multiple signals from the same data, improving productivity without requiring separate training systems.
Data Source
AI summary
A method of training an action selection neural network to perform a demonstrated task using a supervised learning technique. The action selection neural network is configured to receive demonstration data comprising actions to perform the task and rewards received for performing the actions. The action selection neural network has auxiliary prediction task neural networks on one or more of its intermediate outputs. The action selection policy neural network is trained using multiple combined losses, concurrently with the auxiliary prediction task neural networks.