Neural Network Look-Ahead Search for RL Action Selection
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Solution Overview
Problem
Reinforcement learning systems face challenges in effectively selecting actions in very large or complex state spaces, especially when reward signals are sparse, leading to inefficiencies in processing power and memory usage.
Innovation Solution
The method involves training a neural network to perform a look ahead search in the state tree of the environment, guided by current network parameters, to determine a target network output that defines an action selection policy. This approach allows for the selection of actions that maximize the likelihood of achieving a specified result, even in large state spaces, and reduces the computational resources required for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning systems use traditional methods to select actions in large state spaces, then the system can explore the state space, but the processing power and memory requirements become excessively high
Solution Approach 1:
The patent segments the action selection process into two distinct components: a neural network that evaluates action qualities and a search algorithm that explores possible action sequences. This segmentation allows the system to handle large state spaces by dividing the complex decision-making task into manageable parts, reducing the computational burden on any single component while maintaining the ability to explore extensive state spaces.
Solution Approach 2:
The neural network performs preliminary evaluation of actions by computing quality estimates before the search algorithm executes them. This preliminary action filters and prioritizes potential actions, allowing the subsequent search to focus only on promising action sequences. By performing this evaluation advance, the system reduces the effective search space and lowers overall processing requirements while maintaining adaptability to large state spaces.
2Reliability
If reinforcement learning systems use traditional methods to select actions, then the system can learn from reward signals, but the training time and computational resources become excessively high
Solution Approach 1:
The patent introduces a neural network as an intermediary component between the environment and the search algorithm. This intermediary computes quality estimates for actions, providing a bridge that guides the search process. By using this intermediary, the system learns more effectively from sparse reward signals because the neural network can evaluate actions based on predicted outcomes rather than requiring extensive trial-and-error exploration, significantly reducing training time while maintaining learning reliability.
Solution Approach 2:
The system implements feedback mechanisms where the neural network continuously refines its quality estimates based on observed rewards and outcomes. This feedback loop allows the system to learn from each interaction and improve its action selection policy. The feedback mechanism enables efficient learning by adjusting the neural network's evaluations in real-time, reducing the number of iterations needed to converge on optimal policies and thereby reducing overall training time.
3Manufacturing precision
If reinforcement learning systems perform exhaustive search in state trees, then the system can find optimal actions, but the memory requirements become excessively high
Solution Approach 1:
The patent applies local quality by having the neural network evaluate actions based on their specific context and quality characteristics rather than treating all actions uniformly. The search algorithm then focuses computational resources on action sequences with high quality estimates. This localized approach to evaluation and search maintains high action selection accuracy by considering the specific properties of each action while reducing overall memory requirements by not needing to store complete information about all possible state-space combinations.
Data Source
AI summary
Methods, systems and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network. One of the methods includes receiving an observation characterizing a current state of the environment; determining a target network output for the observation by performing a look ahead search of possible future states of the environment starting from the current state until the environment reaches a possible future state that satisfies one or more termination criteria, wherein the look ahead search is guided by the neural network in accordance with current values of the network parameters; selecting an action to be performed by the agent in response to the observation using the target network output generated by performing the look ahead search; and storing, in an exploration history data store, the target network output in association with the observation for use in updating the current values of the network parameters.