Reinforcement Learning Critic Networks for Stable Q-Value Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing reinforcement learning algorithms face challenges in efficiently estimating Q-values, particularly in continuous action spaces, leading to instability and overestimation issues.
Innovation Solution
The proposed method, referred to as 'Std Q-target', incorporates a State Action Learned Embedding (SALE) technique and subtracts the standard deviation of Q-target network values from the Q-target network, providing a more conservative and efficient Q-value estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard reinforcement learning algorithms are used to estimate Q-values in continuous action spaces, then the agent can learn policies, but the Q-value estimation becomes unstable and prone to overestimation
Solution Approach 1:
The patent introduces an embedding encoder as an intermediary component that transforms state-action pairs into latent embeddings. These embeddings serve as a mediator between the raw state-action inputs and the Q-value estimation process, enabling the network to learn meaningful representations that improve both stability and accuracy of Q-value estimation.
Solution Approach 2:
The patent modifies the Q-value estimation by changing the parameter representation through learned embeddings. Instead of directly estimating Q-values from raw state-action pairs, the system transforms inputs into latent embedding spaces where the estimation becomes more stable and accurate, effectively changing the parameter space for better performance.
2Measurement precision
If computationally heavy mechanisms are added to improve Q-value estimation, then estimation accuracy improves, but training time and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-processing state-action pairs through the embedding encoder to create latent embeddings before Q-value estimation. This preliminary transformation of inputs into meaningful representations accelerates the subsequent estimation process while improving accuracy, as the network operates on pre-processed embeddings rather than raw inputs.
Solution Approach 2:
The patent replaces the traditional mechanical Q-value estimation mechanism with a learned embedding-based approach. Instead of relying on complex computational mechanisms for direct estimation, the system substitutes this with a neural network-based embedding layer that learns efficient representations, reducing computational overhead while improving accuracy.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A computer-implemented method of learning a policy for controlling a computercontrolled system, in particular a robot, by reinforcement learning, comprising the steps of: Observing a current state (s) of an environment of the computercontrolled system. Interacting (S12) with the environment by carrying out the steps of: Determining an action (a) by the policy, which is an actor network, depending on the current state (s), Executing the action (a) by the computercontrolled system, receiving a reward (r) and the next state (s'), and storing the interaction with the environment as an experience in a replay buffer. Sampling (13) a batch of experiences from the replay buffer. Computing (S15) Q values by at least two critic networks for the experiences in said batch. Determining a standard derivation of the Q values for the experiences in said batch for each of the critic networks. Updating (S17) critic parameters depending on the Bellman equation and adding the standard derivations as regularization.