Actor-Critic Training With Frozen Critics for Resource-Limited Deployment
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Solution Overview
Problem
Existing reinforcement learning systems face challenges in optimizing actor networks due to limited computational resources in production settings, leading to suboptimal performance and safety issues, especially when laboratory-trained controllers are deployed without further learning.
Innovation Solution
The method involves training an actor network and a critic network using state information from both laboratory and production settings, optimizing the critic network with the actor network, and duplicating it into a frozen critic network to optimize the production actor network, ensuring accurate policy deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large actor network is trained using abundant state information in laboratory settings, then the policy performance is improved, but the computational resource requirements increase making deployment in production settings difficult
Solution Approach 1:
The training process is segmented into two distinct phases: laboratory training with abundant computational resources and state information, and production deployment with constrained resources. The actor-critic network is trained extensively in the laboratory setting, then a simplified version is deployed to production, separating the heavy computational workload from the deployment environment.
Solution Approach 2:
The actor-critic network undergoes preliminary training in the laboratory setting using abundant computational resources and comprehensive state information before deployment. This preliminary action ensures the network achieves high performance offline, so that the production system can operate with simpler, more resource-efficient configurations.
2Device complexity
If the actor network is optimized using limited state information available in production settings, then the deployment complexity is reduced, but the policy performance deteriorates
Solution Approach 1:
The network is pre-trained in the laboratory setting with abundant state information and computational resources, achieving high performance before deployment. This preliminary training ensures that the policy learned offline is robust, allowing the production system to use a simpler actor network that operates effectively with limited state information.
Solution Approach 2:
The critic network, trained extensively in the laboratory, serves as a frozen copy that guides the optimization of the production actor network. This copying approach allows the production system to benefit from the comprehensive training done offline without requiring the full computational resources or state information during deployment.
3Measurement precision
If additional laboratory state information is used for training, then the learning accuracy is improved, but the information availability in production setting remains limited
Solution Approach 1:
The system performs preliminary training in the laboratory setting where comprehensive state information is available, achieving high learning accuracy offline. During production deployment, the actor network operates with limited state information, relying on the robust policy learned during the preliminary laboratory phase.
Solution Approach 2:
The critic network acts as an intermediary that was trained with comprehensive laboratory state information. It provides guidance signals to the production actor network, enabling the actor to make accurate decisions with limited state information by leveraging the knowledge encoded in the frozen critic network during inference.
Data Source
AI summary
Reinforcement learning methods can use actor-critic networks where (1) additional laboratory-only state information is used to train a policy that much act without this additional laboratory-only information in a production setting; and (2) complex resource-demanding policies are distilled into a less-demanding policy that can be more easily run at production with limited computational resources. The production actor network can be optimized using a frozen version of a large critic network, previously trained with a large actor network. Aspects of these methods can leverage actor-critic methods in which the critic network models the action value function, as opposed to the state value function.