MDP Policy Adaptation for Growing Action Sets
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Solution Overview
Problem
Conventional Markov decision process (MDP) models are inadequate for real-world scenarios where the set of available actions grows over time, as they do not account for changing action sets, leading to the need for autonomous systems that can adapt to new actions.
Innovation Solution
The method involves computing a policy function for an MDP based on a state conditional function, an inverse dynamics function, and an action selection function, which are updated when new actions are identified, allowing the system to map new actions into an embedding space and leverage previous learning for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional MDP models are used for decision-making, then the model structure is simple and well-defined, but the system cannot adapt to growing or changing action sets
Solution Approach 1:
The policy function is segmented into multiple independent components: state conditional function, inverse dynamics function, and action selection function. Each component has a specific role and can be updated independently when the action set changes, allowing adaptability without complete model redesign
Solution Approach 2:
The embedding space serves as a universal representation that can accommodate any action from the original set or newly added actions. The inverse dynamics function learns a general mapping from state transitions to action embeddings, making the system universally applicable to expanding action sets
2Adaptability or versatility
If the action set grows over time with new actions added, then the system becomes more versatile and capable, but the computational burden increases
Solution Approach 1:
The inverse dynamics function is trained in advance on the initial action set to learn the mapping from state transitions to action embeddings. When new actions are added, the system leverages this pre-learned representation and only requires incremental updates rather than complete retraining, reducing computational burden
Solution Approach 2:
New actions are mapped into the existing embedding space by leveraging the learned inverse dynamics function, rather than creating entirely new representations. This copying approach allows new actions to be integrated efficiently using the structure already learned from previous actions
3Adaptability or versatility
If the policy function is updated frequently to adapt to new actions, then the system maintains high adaptability, but the time required for updates increases
Solution Approach 1:
The policy function is divided into independent components that can be updated separately. When new actions are added, only the action selection function and relevant portions of the inverse dynamics function need updating, while the state conditional function can remain unchanged, reducing overall update time
Solution Approach 2:
The system maintains continuous learning by incrementally updating the policy components as new actions are introduced, rather than performing discrete complete retraining. This allows the system to adapt continuously with minimal disruption and reduced total update time
Data Source
AI summary
Systems and methods are described for a decision-making process that includes an increasing set of actions, compute a policy function for a Markov decision process (MDP) for the decision-making process, wherein the policy function is computed based on a state conditional function mapping states into an embedding space, an inverse dynamics function mapping state transitions into the embedding space, and an action selection function mapping the elements of the embedding space to actions, identify an additional set of actions in the increasing set of actions, update the inverse dynamics function based at least in part on the additional set of actions, update the policy function based on the updated inverse dynamics function and parameters learned during the computing the policy function, and select an action based on the updated policy function.