Reinforcement Learning Transfer via Value Function Bounding
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Solution Overview
Problem
Reinforcement learning agents face difficulties in responding to tasks with different time and reward functions than those used during their initial training, limiting their performance in new or varying scenarios.
Innovation Solution
The method involves approximating an optimal value-function for a task vector using a value approximator, determining upper and lower bounds, correcting the value-function within these bounds, and determining an optimal policy, which allows for effective transfer learning and zero-shot transition to new tasks without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is trained on specific reward functions at specific times, then the agent achieves high performance on those tasks, but the agent cannot respond to tasks with different reward functions or time
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing task vectors during the training phase. These task vectors capture the essential characteristics of tasks and are stored in a database, enabling the agent to quickly retrieve and adapt to new tasks without retraining. The task vectors are prepared in advance and can be directly applied when new tasks arise, eliminating the need for additional training while maintaining high performance.
Solution Approach 2:
The patent uses copying by creating a database of task vectors that replicates the training data structure. When a new task appears, the system copies the relevant task vector from the database and adapts it to the current task requirements. This copying mechanism allows the agent to leverage previously learned patterns without requiring retraining, thus maintaining both reliability on trained tasks and adaptability to new tasks.
2Adaptability or versatility
If transfer learning is applied to respond to new tasks, then the agent can handle tasks with different reward functions, but the performance may not be optimal without additional training
Solution Approach 1:
The patent applies preliminary action by pre-computing task vectors during training and storing them in a database. When a new task arises, the system retrieves the appropriate task vector from the database and uses it to immediately begin performing the task. This preliminary preparation eliminates the need for additional training while ensuring high performance, as the task vector already contains the optimized policy information from the training phase.
Solution Approach 2:
The patent introduces task vectors as intermediaries between the training data and new tasks. These task vectors serve as mediators that bridge the gap between previously trained tasks and new tasks, enabling smooth transfer of knowledge. The task vectors act as a common representation that can be applied across different tasks without requiring direct retraining, thus maintaining both adaptability and high performance.
3Reliability
If additional training is performed for new tasks, then the agent achieves optimal performance, but the time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing task vectors during the initial training phase. When a new task appears, the system can immediately retrieve the relevant task vector from the database and begin performing the task without any additional training. This preliminary preparation completely eliminates the training time requirement for new tasks, allowing the agent to respond instantly while maintaining optimal performance.
Solution Approach 2:
The patent uses copying by creating a reusable database of task vectors that can be instantly accessed for new tasks. Instead of retraining from scratch, the system copies the pre-computed task vectors from the database and adapts them to the current task. This copying approach reduces training time to zero for new tasks while maintaining optimal performance, as the task vectors are already optimized during the initial training phase.
Data Source
AI summary
A method and device with transferal of reinforcement learning are disclosed. The method includes: approximating an optimal value-function for a task vector using a value approximator trained to output a minimum value-function using a state of an agent and source task vectors; determining an upper bound of the optimal value-function for the task vector; determining a lower bound of the optimal value-function for the task vector; correcting the optimal value-function for the task vector based on the upper bound and the lower bound; and determining an optimal policy for the task vector using the corrected optimal value-function for the task vector.