Sparse RL Training With SAM and Gradient Clipping
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Solution Overview
Problem
Current lifelong deep reinforcement learning methods face challenges with increased resource consumption and model size due to the addition of new tasks, leading to catastrophic forgetting and limited generalization abilities.
Innovation Solution
A training method utilizing sparse training-cropped sharpness awareness minimization with momentum (ST-CSAMM) optimizer, which includes gradient clipping and sharpness awareness minimization calculation to update neural networks, reducing model size and improving generalization by preserving important previous experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model architecture extension or continuous knowledge memory is used in replay buffer, then lifelong DRL can adapt to new tasks, but resource consumption and model size increase proportionally with number of tasks
Solution Approach 1:
The patent extracts and removes redundant or less important task information from the replay buffer, keeping only essential experiences. This selective extraction allows the model to adapt to new tasks while preventing unbounded growth of model size, as the replay buffer maintains a curated subset of important experiences rather than storing all task data indefinitely
Solution Approach 2:
The patent changes the parameter of replay buffer capacity from unbounded to bounded by implementing a fixed-size buffer with selective sampling mechanisms. This parameter change transforms the system from continuously growing memory to a fixed-resource system that prioritizes important experiences, thereby maintaining adaptability while controlling model size
2Adaptability or versatility
If more tasks are learned in lifelong DRL, then task versatility increases, but catastrophic forgetting of previous knowledge occurs
Solution Approach 1:
The patent implements feedback mechanisms where the performance on previous tasks is continuously monitored during training on new tasks. When degradation is detected, the system adjusts the replay sampling strategy to prioritize experiences from tasks showing forgetting, creating a feedback loop that actively prevents catastrophic forgetting while allowing continuous learning of new tasks
Solution Approach 2:
The patent performs preliminary actions by pre-processing and prioritizing experiences in the replay buffer before they are needed for training. Important experiences are identified and weighted in advance, ensuring that critical knowledge from previous tasks is readily available when training on new tasks, thereby preventing forgetting before it occurs
3Reliability
If gradient clipping is performed before sharpness awareness minimization, then gradient explosion is avoided, but computational complexity increases
Solution Approach 1:
The patent segments the optimization process into distinct stages: first performing gradient clipping to bound gradient norms, then performing sharpness awareness minimization on the clipped gradients. This segmentation allows each operation to be optimized independently and prevents the compounding of computational costs that would occur if both operations were applied simultaneously to full-precision gradients
Data Source
AI summary
A training method includes the following steps for each time step included in one or more episode. An action is generated by a sparse agent according a state. Candidate samples are obtained from an experience replay buffer, to update a current neural network of the sparse agent. The step for updating the current neural network includes the following steps. A loss function is calculated according to the candidate samples. Gradients of the loss function with respect to weights are calculated. Perform gradient clipping on the gradients to generate adjusted gradients. Perform sharpness awareness minimizes (SAM) calculation on the adjusted gradients to obtain perturbation vectors. Update the current neural network according to the loss function and the perturbation vectors to output an updated neural network.