Neural Network Value Function Training via Importance Weighting
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Solution Overview
Problem
Off-policy reinforcement learning methods face challenges in high-dimensional action spaces due to the complexity of learning a Q function, while on-policy methods fail to fully utilize data, making it desirable to effectively learn a state-value function for efficient training of control policies.
Innovation Solution
A method is developed to train a neural network to implement a value function that predicts cumulative rewards, adapting the network to reduce loss by weighting deviations in actions based on their likelihoods between the control policy and behavior policy, prioritizing actions leading to higher-valued states, and using importance weights to optimize the value function, allowing for efficient training with off-policy samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If off-policy methods are used to make full use of data, then data efficiency is improved, but learning a Q function becomes complex and undesirable for high-dimensional action spaces
Solution Approach 1:
The patent extracts the action dependency from the value function estimation, transitioning from learning a Q function Q(s,a) to learning a V function V(s) only. This removes the complexity of handling high-dimensional action spaces while maintaining off-policy learning capabilities through importance sampling weights.
Solution Approach 2:
The patent changes the parameters being learned by the neural network from state-action pairs (s,a) to only states (s). This parameter reduction simplifies the learning problem for high-dimensional action spaces while the importance sampling mechanism preserves the off-policy data utilization advantage.
2Ease of manufacture
If on-policy methods are used to learn a state-value function more easily, then ease of learning is improved, but data utilization is not full
Solution Approach 1:
The patent introduces importance sampling weights as an intermediary mechanism that bridges on-policy and off-policy approaches. These weights allow the use of off-policy data (generated by behavior policies) while maintaining the simpler V function learning objective characteristic of on-policy methods.
3Measurement precision
If importance sampling weights are used to account for policy differences, then accuracy of value estimation is improved, but variance in loss function increases
Solution Approach 1:
The patent applies partial importance sampling by truncating extreme weight values and using clipped versions of the importance ratios. This partial application reduces the variance introduced by extreme weights while maintaining the corrective effect of importance sampling for accurate value estimation.
Data Source
AI summary
A method for training a control policy for controlling a technical system. The method includes training a neural network to implement a value function by: adapting the neural network for reducing a loss which, for a plurality of states and, for each state, for at least one action that has been previously carried out in the state, involves a deviation between a prediction for a cumulative reward and an estimation of the cumulative reward that is ascertained from a subsequent state that has been achieved by the action, and a reward that is obtained by the action. In the loss, for each action, the deviation for the action is weighted more strongly the greater the likelihood is that the action is selected by the control policy, in relation to the likelihood that the action is selected by a behavior control policy. The method also includes training the control policy.