Mitigating Delusional Bias in Deep Q-Learning via CONQUR
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Solution Overview
Problem
Delusional bias in Q-learning leads to unbounded approximation error, divergence, and policy cycling due to mutually inconsistent estimates of successor-state values, making it challenging to achieve robustness and scalability in deep reinforcement learning.
Innovation Solution
The CONQUR framework integrates policy-consistent backups with regression-based function approximation, using a soft-consistency penalty and a search framework to mitigate delusional bias by promoting consistency across training batches and managing multiple information sets, ensuring convergence while conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional Q-learning updates value estimates using successor-state values, then learning speed improves, but delusional bias causes unbounded approximation error and divergence
Solution Approach 1:
The patent implements a feedback mechanism where the policy regressor continuously monitors and enforces consistency between Q-value updates and policy decisions. The consistency penalty term feeds back into the loss function, adjusting Q-values that violate policy consistency, thereby preventing delusional bias while maintaining learning progress.
Solution Approach 2:
The patent applies preliminary action by enforcing policy consistency constraints before Q-value updates can cause delusional bias. The consistency penalty is incorporated into the loss function in advance, preventing inconsistent value estimates from developing during training, thus ensuring convergence stability before problems arise.
2Reliability
If policy consistency constraints are enforced to eliminate delusional bias, then convergence stability improves, but computational complexity increases
Solution Approach 1:
The patent changes the parameter space by introducing a consistency penalty term into the loss function, transforming the optimization problem to include policy consistency constraints. This parameter modification enables enforcement of convergence stability through a mathematically tractable approach that scales with the existing Q-learning framework.
Solution Approach 2:
The patent introduces an intermediary consistency penalty term that mediates between Q-value updates and policy decisions. This intermediary mechanism translates policy consistency requirements into a differentiable loss component, enabling gradient-based optimization without requiring complex discrete constraints or exhaustive search procedures.
3Reliability
If multiple candidate value networks are tracked using search framework, then policy consistency improves, but memory and processing resources increase
Solution Approach 1:
The patent merges the policy consistency enforcement mechanism with the standard Q-learning update rule by incorporating the consistency penalty directly into the loss function. This combination eliminates the need for separate tracking of multiple candidate value networks, as consistency is enforced continuously during a single training process, reducing memory requirements while maintaining policy consistency.
Data Source
AI summary
Delusional bias can occur in function approximation Q-learning. Techniques for training and/or using a value network to mitigate delusional bias is disclosed herein, where the value network can be used to generate action(s) for an agent (e.g., a robot agent, a software agent, etc.). In various implementations, delusional bias can be mitigated by using a soft-consistency penalty. Additionally or alternatively, delusional bias can be mitigated by using a search framework over multiple Q-functions.