Reinforcement Learning Value Estimation With Eligibility Traces
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Solution Overview
Problem
Reinforcement learning systems face challenges in accurately tracking observations that lead to later rewards due to time delays, memory inefficiencies, and computational scaling issues, with existing methods like backpropagation through time, table-based systems, Temporal Difference learning, and Monte Carlo-evaluations being inadequate.
Innovation Solution
A system utilizing a differentiable estimator, eligibility trace generators, and bootstrapped gradient estimators to efficiently estimate the gradient of discounted future rewards, employing exponential decay and bootstrapping to optimize value estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If backpropagation through time is used to track observations leading to later rewards, then measurement precision of reward attribution is improved, but memory usage and computational complexity increase significantly
Solution Approach 1:
The patent segments the value estimation process into multiple independent estimators, each responsible for a specific time step. This allows the system to track reward attribution through time without requiring backpropagation through the entire sequence, reducing memory usage while maintaining precision through localized gradient estimation at each time step.
Solution Approach 2:
The patent pre-calculates and stores gradient information in eligibility traces before reward signals are received. By maintaining these traces in advance, the system can efficiently attribute rewards to specific observations without requiring complex backpropagation when rewards arrive, thus improving measurement precision while controlling computational complexity.
2Productivity
If Temporal Difference learning is used for local value estimation at each time step, then computational efficiency is improved, but accuracy deteriorates due to inability to propagate rewards over long time delays
Solution Approach 1:
The patent merges TD-learning's computational efficiency with eligibility trace methods by combining local value estimation at each time step with pre-computed gradient traces. This hybrid approach maintains the efficiency of local updates while preserving the ability to accurately attribute rewards across long time delays through the stored eligibility information.
3Measurement precision
If Monte Carlo-evaluations are used to estimate future rewards, then accuracy of long-term reward prediction is improved, but data efficiency deteriorates due to lack of bootstrapping
Solution Approach 1:
The patent introduces bootstrapping feedback into the value estimation process by using current value estimates to update future value predictions. This allows the system to learn from its own predictions while maintaining the accuracy benefits of Monte Carlo methods, significantly improving data efficiency by reusing existing value information rather than requiring complete new trajectories.
4Measurement precision
If value estimators are updated through multiple passes or iterations to handle small time steps, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent pre-computes eligibility traces that encode gradient information for future value updates. This preliminary action allows single-pass or few-pass updates to achieve the same precision that would otherwise require multiple iterations, significantly reducing training time while maintaining value estimation accuracy through the pre-prepared trace information.
Data Source
AI summary
A system for supporting machine learning having one or more processors; a memory configured to store one or more parameters of an estimator; a differentiable estimator of discounted reward, referred to as a value estimator, configured to receive an observation including information representative of one or more observations of an environment and/or system, access parameters of the value estimator from memory and generate a discounted reward estimate, referred to as a value estimate. The system includes one or more eligibility trace generators configured to receive the value estimate; receive from the value estimator a partial derivative of the value estimate with respect to one of the parameters of a value estimation process; receive a signal from the eligibility trace generator and generate an updated signal, referred to as an eligibility trace. The system having one or more bootstrapped gradient estimators configured to receive a reward signal from a technical reward system; receive the value estimate and the eligibility trace; and create a gradient estimate based on an input including: the reward; the eligibility trace; the value estimate; and the gradient of the value estimate.