Adaptive Modulation Engine for Agent Learning Efficiency
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Solution Overview
Problem
Existing neural network systems for controlling agents in environments rely on fixed exploration policies, which can lead to inefficient learning and resource consumption during training, as they do not adapt to the task or the agent's skill level.
Innovation Solution
A system that uses an adaptive modulation engine to adjust the exploration policy by sampling behavior modulation factors from a probability distribution, optimizing learning progress and reducing computational resources by dynamically tailoring exploration to the task and agent's skill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed exploration policy is used, then the agent behavior is stable and predictable, but the learning efficiency is low and training takes more iterations
Solution Approach 1:
The patent applies dynamics by transforming the static exploration policy into a dynamic one that adapts over time. The behavior modulation factor is updated iteratively during training based on the agent's current skill level and task requirements, allowing the exploration policy to evolve from random to structured as the agent learns, thereby improving learning efficiency while maintaining adaptability
Solution Approach 2:
The patent changes the parameter of the exploration policy by introducing a behavior modulation factor that adjusts the probability distribution over actions. This parameter is dynamically modified during training to balance exploration and exploitation, enabling the agent to efficiently learn tasks by adapting its behavior policy based on current performance and task characteristics
2Productivity
If exploration is increased to improve learning, then task learning speed increases, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by selectively modulating exploration only when beneficial for learning. The behavior modulation factor is adjusted based on the agent's current skill level, providing stronger exploration modulation when the agent is novice and reducing it as the agent becomes more skilled, thereby optimizing computational resource usage while maintaining effective learning speed
Solution Approach 2:
The patent implements feedback by using the agent's performance and skill level to dynamically adjust the behavior modulation factor. The system monitors learning progress and adapts the exploration policy accordingly, reducing computational waste by decreasing exploration modulation as the agent improves, thus optimizing the balance between learning speed and resource consumption
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent. One of the methods includes sampling a behavior modulation in accordance with a current probability distribution; for each of one or more time steps: processing an input comprising an observation characterizing a current state of the environment at the time step using an action selection neural network to generate a respective action score for each action in a set of possible actions that can be performed by the agent; modifying the action scores using the sampled behavior modulation; and selecting the action to be performed by the agent at the time step based on the modified action scores; determining a fitness measure corresponding to the sampled behavior modulation; and updating the current probability distribution over the set of possible behavior modulations using the fitness measure corresponding to the behavior modulation.