Reinforcement Learning Event Buffers for Faster Exploration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reinforcement learning faces challenges such as requiring extensive data for convergence, inability to prioritize important events, treating all events equally, and being represented in non-human understandable formats, leading to inefficiencies in exploration and exploitation balance.
Innovation Solution
Implementing an experience buffer to store non-critical experiences and an event buffer to store critical experiences, with actions from the event buffer sampled during exploration, and network probing to understand decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning uses traditional exploration-exploitation balance, then it can learn from trial and error, but it requires a lot of data to converge and involves a model that treats all events equally irrespective of whether or not the events are considered important
Solution Approach 1:
The patent segments the experience buffer into two distinct parts: a first buffer for storing critical experiences (those with high rewards or important events) and a second buffer for storing non-critical experiences. This segmentation allows the system to prioritize replay of critical experiences during training, thereby improving convergence speed without requiring proportionally more data overall. The segmented structure enables selective sampling that focuses computational resources on the most valuable learning opportunities.
Solution Approach 2:
The patent applies local quality by treating different experiences differently based on their importance. Critical experiences (those exceeding a reward threshold or meeting importance criteria) are stored in a separate buffer and sampled with higher probability during replay. This local differentiation in treatment allows the system to focus learning attention on important events while still maintaining a record of other experiences, thereby improving efficiency without losing information about less critical events.
2Adaptability or versatility
If reinforcement learning treats all events equally, then the model is simple to implement, but it is unable to prioritize important events and involves a model that treats all events equally irrespective of whether or not the events are considered important
Solution Approach 1:
The experience buffer is segmented into a first buffer for critical experiences and a second buffer for non-critical experiences. This segmentation enables the system to prioritize important events during replay without requiring a completely complex model architecture. The segmentation itself provides the adaptability to differentiate event importance while keeping the overall structure relatively simple and manageable.
Solution Approach 2:
The patent introduces a reward threshold parameter and an importance criterion parameter that allow the system to adaptively differentiate between critical and non-critical experiences. By changing these parameters, the system can adjust what constitutes an important event without fundamentally altering the model structure. This parameter-based approach provides adaptability while maintaining reasonable model complexity.
3Productivity
If reinforcement learning uses neural networks for representation, then it can learn complex patterns, but it being mostly represented as neural networks (that is, not in a human understandable format)
Solution Approach 1:
The patent introduces an intermediary mechanism - the structured experience buffer with critical experience identification - that bridges the gap between neural network learning and human understandability. By explicitly identifying and storing critical experiences with their associated states, actions, and rewards in a structured format, the system creates an intermediate representation that is more interpretable than raw neural network weights while still enabling complex pattern learning. This intermediary layer allows researchers to analyze what the system learned without directly interpreting neural network parameters.
Data Source
AI summary
A computer-implemented method is provided for reinforcement learning performed by a processor. The method includes obtaining, from an environment, a given experience that includes an action, a state and a reward. The method further includes storing the given experience in an experience buffer responsive to a value of the reward included in the given experience exceeding a first threshold. The method also includes responsive to obtaining another experience having another reward that less than or equal to the first threshold, searching the experience buffer for a candidate experience with a similar state to the other experience and copying the candidate experience into an event buffer. The method additionally includes during exploration, selecting an action to be taken to the environment from the event buffer with a predetermined probability.