Adaptive Exploration Mechanism for Deep Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep reinforcement learning techniques, such as DQN, require extensive trial and error for learning optimal actions, with learning time and final solutions being sensitive to the exploration term, which is often fixed or annealed using ad hoc mechanisms.
Innovation Solution
Adaptive adjustment of the exploration term (ε) using an information-theoretic mechanism that learns an inverse temperature parameter (β) over time with gradient descent, allowing the ε-greedy policy to be dynamically updated based on continuous performance of a deep neural network function approximator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed or ad hoc annealed exploration term is used, then the learning process is stable and easy to implement, but the learning time is long and the solution quality is sensitive to parameter tuning
Solution Approach 1:
The patent applies dynamics by making the exploration term adaptive rather than fixed. The exploration term is updated dynamically based on the performance of the function approximator, allowing the system to automatically adjust exploration behavior during learning. This resolves the contradiction by enabling faster learning through adaptive exploration while maintaining stability through performance-based updates.
Solution Approach 2:
The patent implements feedback by using the performance of the function approximator to update the exploration term. The system continuously monitors learning performance and adjusts the exploration term accordingly, creating a closed-loop control mechanism. This feedback loop enables the system to accelerate learning by increasing exploration when needed while reducing it when the model is confident, thereby reducing overall learning time.
2Reliability
If extensive trial and error exploration is performed, then optimal actions can be learned, but the learning time increases significantly
Solution Approach 1:
The patent makes the exploration term dynamic and adaptive, allowing the system to perform extensive exploration only when necessary. By updating the exploration term based on function approximator performance, the system can intensify exploration during early learning phases when solution quality is poor, then reduce exploration as the model improves, thereby maintaining solution quality while reducing overall learning time.
Solution Approach 2:
The patent changes the exploration parameter adaptively during learning rather than keeping it fixed. The exploration term is modified based on performance metrics, allowing the system to optimize the balance between exploration and exploitation. This parameter adaptation enables the system to achieve high solution quality by performing sufficient exploration early on, then converging faster by reducing exploration as learning progresses.
3Ease of manufacture
If the exploration term is fixed or ad hoc annealed, then the implementation is simple, but the final solution is sensitive to parameter choices
Solution Approach 1:
The patent implements self-service by enabling the system to automatically adjust its own exploration term based on performance feedback. Instead of requiring manual parameter tuning or ad hoc annealing schedules, the system autonomously adapts the exploration term through performance-based updates. This maintains implementation simplicity while dramatically improving adaptability, as the system automatically adjusts to the specific learning task and conditions.
Solution Approach 2:
The patent transforms the exploration parameter from a static or manually-tuned value to a dynamically adapting parameter. By linking parameter changes to performance metrics, the system eliminates the need for careful manual parameter selection while gaining adaptability to different learning scenarios. This approach maintains ease of implementation through automated updates while achieving high adaptability to various tasks and conditions.
Data Source
AI summary
Adaptive exploration in deep reinforcement learning may be performed by inputting a current time frame of an action and observation sequence sequentially into a function approximator, such as a deep neural network, including a plurality of parameters, the action and observation sequence including a plurality of time frames, each time frame including action values and observation values, approximating a value function using the function approximator based on the current time frame to acquire a current value, updating an action selection policy through exploration based on an ε-greedy strategy using the current value, and updating the plurality of parameters.