Agent Curricula for Bootstrapped Complex Reinforcement Learning
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Solution Overview
Problem
Reinforcement learning systems face challenges in efficiently training complex neural networks to perform tasks due to high computational requirements and prolonged training times, especially when these networks require more training steps or are less robust to hyperparameter settings.
Innovation Solution
A curriculum-based approach is employed, where a set of candidate agent policy neural networks with varying complexity levels are trained jointly, with initial weights favoring simpler networks, gradually transitioning to more complex networks, leveraging simpler networks' solutions to accelerate the learning process of more complex networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a complex neural network is trained to perform reinforcement learning tasks, then the agent can achieve higher performance capabilities, but the training time and computational resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by training simpler neural networks before the complex final network. The simpler networks are trained first to learn basic task components, and their learned policies are then used as initialization or guidance for training the more complex final network, thereby reducing the overall training time required to achieve high performance
Solution Approach 2:
The training process is segmented into multiple stages with different network complexities. Instead of training one complex network from scratch, the patent divides the learning process into phases where simpler networks handle easier subtasks first, and the complex network gradually takes over, breaking down the complex training problem into manageable segments
2Adaptability or versatility
If a complex neural network with more parameters or larger action space is used, then the agent can perform more sophisticated tasks, but the training becomes more difficult and requires more training steps
Solution Approach 1:
Simpler networks with fewer parameters are trained first to establish basic policy structures. These pre-trained simpler networks provide a head start for the complex network, reducing the difficulty of training sophisticated policies in the complex network without sacrificing task complexity handling capability
Solution Approach 2:
The patent changes parameters progressively by adjusting network complexity, learning rates, and other hyperparameters across different training stages. The complex network is trained with parameter settings that leverage knowledge from simpler networks, making the training process more manageable despite the increased network complexity
3Manufacturing precision
If training is performed on a single complex network in isolation, then the network can be optimized for the final task, but the training becomes prohibitively expensive and time-consuming
Solution Approach 1:
The training workload is segmented across multiple simpler networks that are trained in parallel or sequence before the final complex network. This segmentation distributes the computational burden, reducing the energy required to train any single network while maintaining overall policy optimization quality through the progressive knowledge transfer
Solution Approach 2:
The patent uses copying by initializing the complex network with parameters or policies learned from simpler networks. Instead of training the complex network from random initialization (which is computationally expensive), the system copies learned representations from simpler networks, significantly reducing the computational resources needed while preserving optimization quality
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning using agent curricula.