Cross-Entropy Guided Policy for Stable Continuous Action Control
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Solution Overview
Problem
Off-policy Q-learning methods for continuous action domains in robotics face challenges such as training instability, hyperparameter sensitivity, and runtime constraints, making it difficult to achieve stable and efficient control in real-world robotics tasks.
Innovation Solution
The proposed solution involves training a cross-entropy guided policy (CGP) neural network and a q-gradient guided policy (QGP) neural network separately from the Q-function neural network, using a cross-entropy method (CEM) to parameterize and update the policies, which allows for more stable training and efficient inference without relying on expensive sample iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If off-policy Q-learning methods are used for continuous action domains, then data can be re-used during training and on-robot training becomes feasible, but training stability deteriorates and hyperparameter sensitivity increases
Solution Approach 1:
The patent separates the policy network and Q-function network into independent training processes. The policy network is trained using cross-entropy method with sampled actions, while the Q-function is trained separately using standard Q-learning. This segmentation eliminates the coupling that causes instability in methods like DDPG, allowing each network to converge independently while maintaining data reusability through off-policy training.
Solution Approach 2:
The patent introduces an intermediary CEM policy network that generates sampled actions as a bridge between the state and the Q-function evaluation. This intermediary allows the system to use re-play buffer data effectively while maintaining training stability, as the CEM policy provides consistent action sampling without requiring tight coupling between policy and value networks.
2Adaptability or versatility
If standard Q-learning methods are used for continuous actions, then the system can handle continuous action domains, but the computational runtime increases due to expensive sample iterations
Solution Approach 1:
The patent performs preliminary action sampling using the CEM policy network during the training phase. By pre-computing and storing sampled actions in the replay buffer during training, the system avoids expensive iterative sampling during inference. This preliminary action generation enables fast real-time control while maintaining continuous action capability.
Solution Approach 2:
The patent creates a copy of the policy network (CEM policy network) that is trained separately from the main policy. This copied network is used specifically for generating training samples and can be evaluated in parallel, reducing the computational burden on the main policy network during inference and improving overall computational efficiency.
3Adaptability or versatility
If policy networks are trained with tight coupling to Q-functions, then the system can learn coordinated policies, but the training complexity and hyperparameter sensitivity increase
Solution Approach 1:
The patent divides the training process into segmented, independent phases: CEM policy network training, Q-function training, and main policy training. Each phase has its own loss function and update rules, eliminating the need for complex coordinated updates. This segmentation reduces training complexity while still achieving coordinated policy-Q learning through the shared replay buffer and sequential training pipeline.
Data Source
AI summary
An apparatus for performing continuous actions includes a memory storing instructions, and a processor configured to execute the instructions to obtain a first action of an agent, based on a current state of the agent, using a cross-entropy guided policy (CGP) neural network, and control to perform the obtained first action. The CGP neural network is trained using a cross-entropy method (CEM) policy neural network for obtaining a second action of the agent based on an input state of the agent, and the CEM policy neural network is trained using a CEM and trained separately from the training of the CGP neural network.