Reinforcement Learning Action Slate Selection via Deep Neural Network
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Solution Overview
Problem
Existing reinforcement learning systems face challenges in effectively selecting action slates from high-dimensional observations, especially when the space of possible actions is large and includes null actions.
Innovation Solution
The system employs a slate Q network, a deep neural network, to process observations and candidate action slates, generating slate Q values that estimate long-term rewards. This allows the system to optimize both combinatorial and sequential aspects of action slate selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the action space is expanded to include multiple actions and null actions, then the system's ability to handle complex decision-making is improved, but the complexity of selecting optimal action slates increases
Solution Approach 1:
The patent segments the action selection process into multiple independent neural network components: an action slate selection network that selects which actions to include in the slate, and an action selection network that selects which action to execute from the slate. This segmentation allows each component to focus on a specific aspect of the decision-making process, reducing overall complexity while handling large action spaces effectively.
2Measurement precision
If deep neural networks with multiple hidden layers are used to process high-dimensional observations, then the system's ability to extract features is improved, but the computational requirements and training difficulty increase
Solution Approach 1:
The patent implements preliminary action by using a dedicated action slate selection neural network that processes high-dimensional observations before the main action selection network. This preliminary network pre-processes the input data and selects relevant actions to include in the slate, reducing the dimensionality and complexity of the subsequent action selection process while preserving important features.
3Adaptability or versatility
If the system provides slates of multiple actions to an action selector, then the flexibility of action selection is improved, but the difficulty of optimizing long-term rewards increases
Solution Approach 1:
The patent implements feedback mechanisms where the action selection network receives information about the selected action slate and the resulting environment state transitions. This feedback is used to update the neural network parameters through reinforcement learning, allowing the system to optimize long-term rewards by learning from past decisions. The feedback loop enables continuous improvement of the action selection policy while maintaining flexibility in handling multiple actions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting action slates using reinforcement learning. One of the methods includes receiving an observation characterizing a current state of an environment; selecting an action slate by processing the observation and a plurality of candidate action slates using a deep neural network, wherein each candidate action slate comprises a respective plurality of actions from the set of actions, and wherein the deep neural network is configured to, for each of the candidate action slates, process the observation and the actions in the candidate action slate to generate a slate Q value for the candidate action slate that is an estimate of a long-term reward resulting from the candidate action slate being provided to the action selector in response to the observation; and providing the selected action slate to an action selector in response to the observation.