Offline Q-Network Training With Scaled Temporal Difference Targets
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Solution Overview
Problem
Conventional offline reinforcement learning algorithms face challenges with extrapolation errors and over-estimation of state-action pairs not well-covered by training data, leading to unbounded learning targets and hindered training effectiveness.
Innovation Solution
A regularized offline learning scheme using a scaling term to bound temporal difference learning targets and incorporate ranking regularization, which mitigates extrapolation errors by encouraging more thorough updates from well-performing training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional offline reinforcement learning algorithms are used to train Q neural networks, then training can be performed on pre-collected data without real-time environment interaction, but extrapolation errors occur and learning targets become unbounded leading to poor training effectiveness
Solution Approach 1:
The patent transforms the Q-value estimation problem by changing the parameterization approach: instead of directly estimating absolute Q-values, the method estimates relative Q-values (differences between actions) and uses a scaling term to bound the estimates. This parameter transformation prevents extrapolation errors from causing unbounded learning targets while maintaining training effectiveness on pre-collected data.
Solution Approach 2:
The patent introduces a scaling term as an intermediary parameter that mediates between the raw Q-value estimates and the learning targets. This scaling term acts as a bound that prevents extrapolation errors from propagating uncontrollably, thereby improving measurement precision without sacrificing the ability to train on offline data.
2Adaptability or versatility
If more training data is collected to cover more state-action pairs, then generalization improves, but data collection time and computing resources increase
Solution Approach 1:
By changing from absolute Q-value parameterization to relative Q-value parameterization with scaling, the method achieves better generalization with less data. The relative parameterization focuses learning on actionable differences rather than absolute values, making the policy more adaptable to unseen state-action pairs while reducing the data needed for training.
Solution Approach 2:
The patent applies partial action by using only the necessary component of Q-value estimation (relative differences) rather than full absolute value estimation. This partial approach suffices for policy optimization while reducing computational overhead and data requirements, achieving good generalization without exhaustive data collection.
3Productivity
If the Q neural network is updated frequently with new data, then learning speed increases, but extrapolation errors compound leading to unstable training
Solution Approach 1:
The patent implements feedback through the scaling term that continuously monitors and bounds Q-value estimates during training. This feedback mechanism prevents extrapolation errors from compounding by providing corrective constraints, allowing frequent updates without sacrificing training stability. The scaling term adapts based on observed errors, creating a self-regulating training process.
Solution Approach 2:
The method applies beforehand cushioning by pre-establishing bounds on Q-value estimates through the scaling term before extrapolation errors can compound. This prior constraint prevents error accumulation during frequent updates, maintaining training stability while preserving fast learning speed through continued frequent parameter updates.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to select actions performed by an agent interacting with an environment by performing actions that cause the environment to transition states. One of the methods includes maintaining a replay memory storing a plurality of transitions; selecting a plurality of transitions from the replay memory; and training the neural network on the plurality of transitions, comprising, for each transition: generating an initial Q value for the transition; determining a scaled Q value for the transition; determining a scaled temporal difference learning target for the transition; determining an error between the scaled temporal difference learning target and the scaled Q value; determining an update to the current values of the Q network parameters; and determining an update to the current value of the scaling term.