Policy Neural Network for Dynamic Action Spaces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional reinforcement learning models are not suitable for dynamic action sets, leading to catastrophic forgetting and cold-start issues, as they require redesigning neural network architectures or manual transfer of information when the action space changes.
Innovation Solution
A decision-making agent with a policy neural network trained using topology loss to constrain changes in the action embedding space, allowing for the incorporation of new actions while preserving previous learning, and utilizing adaptive topology and reward-aware embeddings to maintain high-quality action embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard reinforcement learning frameworks are used with changing action sets, then the system can handle new actions, but catastrophic forgetting occurs and previous learning is lost
Solution Approach 1:
The patent applies preliminary action by pre-training the policy neural network on a initial action set before new actions are introduced. This pre-established knowledge base serves as a foundation that prevents catastrophic forgetting when the action space expands, as the network has already consolidated learning patterns for existing actions before encountering new ones.
Solution Approach 2:
The patent segments the action space into distinct action sets (initial action set and new action set) that are handled separately during training. By dividing the learning process into phases where each action set is processed independently with appropriate weighting, the system maintains stability for learned actions while adapting to new actions, preventing interference between different action categories.
2Adaptability or versatility
If the action space is redesigned to accommodate new actions, then the system can adapt to changing environments, but the complexity of the neural network architecture increases
Solution Approach 1:
The patent implements universality by designing a single policy neural network architecture that can handle both initial and new action sets without requiring separate networks or complex redesign. The network uses a unified structure with dynamic action space capabilities, allowing it to perform multiple functions (handling static and dynamic actions) with the same architecture, thus avoiding increased complexity.
Solution Approach 2:
The patent applies dynamics by making the action space of the neural network dynamic rather than static. The network architecture remains fixed, but the action space it operates on changes over time as new actions are introduced. This dynamic approach allows the system to adapt to changing environments without modifying the underlying network structure, maintaining architectural simplicity.
3Reliability
If manual transfer of information is performed when action space changes, then previous learning can be preserved, but the time and computational resources required increase significantly
Solution Approach 1:
The patent implements self-service by enabling the policy neural network to automatically preserve and transfer learning when the action space changes, without requiring manual intervention. The network inherently maintains knowledge of previous actions through its training mechanism and continues to operate effectively when new actions are added, eliminating the need for manual information transfer processes.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining the network's learning capability across action space changes. The training process continues uninterrupted as new actions are introduced, with the network continuously refining its policy while preserving existing knowledge. This continuous learning approach eliminates breaks in the learning process that would otherwise require manual intervention.
Data Source
AI summary
Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.