Reinforcement Learning Epistemic Value Estimation
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Solution Overview
Problem
Reinforcement learning systems face challenges in accurately predicting rewards due to 'return uncertainty' and 'epistemic uncertainty', leading to suboptimal action selection and exploration strategies that may result in inefficient task completion.
Innovation Solution
The system employs a neural network trained using a distribution of value function parameters, updating model parameters based on a posterior distribution derived from a database of actions and rewards, incorporating knowledge parameters to account for epistemic uncertainty, and using sampled values to estimate target rewards, thereby improving the estimation of reward distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reinforcement learning systems use single approximate values for model parameters, then the system complexity is reduced, but the measurement precision of reward prediction deteriorates due to return uncertainty and epistemic uncertainty
Solution Approach 1:
The patent transforms the representation of model parameters from single approximate values to probability distributions characterized by mean values and uncertainty parameters. This parameter change allows the system to explicitly model both return uncertainty and epistemic uncertainty, improving reward prediction precision while maintaining computational tractability through analytical solutions.
Solution Approach 2:
The patent adds an uncertainty dimension to the parameter representation by introducing probability distributions instead of scalar values. This dimensional expansion captures epistemic uncertainty through the distribution parameters, enabling more precise reward predictions without exponentially increasing system complexity.
2Ease of operation
If reinforcement learning systems use exploration strategies based on single parameter values, then the ease of operation is improved, but the reliability of action selection deteriorates due to suboptimal exploration
Solution Approach 1:
The patent incorporates feedback from uncertainty estimates into the action selection process. By using the posterior distribution of parameters to guide exploration, the system dynamically adjusts its behavior based on epistemic uncertainty, improving action selection reliability while maintaining operational simplicity through automated uncertainty-driven exploration.
Solution Approach 2:
The system performs self-service by automatically using its own uncertainty estimates to guide exploration without requiring external intervention or complex manual tuning. The epistemic uncertainty inherent in the parameter distributions naturally drives efficient exploration, improving reliability while keeping the system easy to operate.
3Device complexity
If reinforcement learning systems use conventional training methods without distinguishing uncertainty types, then the device complexity is reduced, but the productivity of training deteriorates due to inefficient task completion
Solution Approach 1:
The patent segments uncertainty into two distinct components: return uncertainty and epistemic uncertainty. This segmentation allows the training process to specifically target and reduce epistemic uncertainty through directed exploration, improving training efficiency without requiring complex additional mechanisms beyond the probabilistic parameter representation.
Solution Approach 2:
The patent performs preliminary action by using epistemic uncertainty estimates to guide exploration before actual task execution. This preliminary uncertainty-driven exploration ensures more efficient task completion by directing attention to informative states, improving productivity while maintaining relatively simple device architecture.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a control system of selecting an action to be performed by a reinforcement learning agent, based on an observation characterizing a current state of an environment. The control system is trained based on a distribution of neural network model parameters derived using a database of previous experiences in the environment.