Reinforcement Learning Opponent Selection for Versatile Agent Training
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Solution Overview
Problem
Existing reinforcement learning methods struggle to generate a general-purpose model that can acquire rewards from various opponents, as they often focus on specific opponents that are easier to win, leading to a lack of versatility.
Innovation Solution
A learning device and method that evaluates the strengths of multiple opponents, sets competitive probabilities based on these strengths, and adjusts the opponents to be faced during reinforcement learning, ensuring a balanced learning environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the opponent is replaced in accordance with a certain learning step, then learning efficiency is improved, but the versatility of the learning model deteriorates
Solution Approach 1:
The patent changes the parameter of opponent selection from fixed periodic replacement to probability-based dynamic selection. The strength evaluation parameter and competitive probability parameter are introduced to control which opponent the learning target faces, allowing the system to adaptively adjust opponent selection based on current learning state rather than following a rigid schedule.
Solution Approach 2:
The opponent replacement mechanism transitions from a static periodic schedule to a dynamic probabilistic process. The competitive probability is calculated based on evaluated strength, making the opponent selection flexible and adaptive to the learning model's current performance level, thereby resolving the contradiction between learning efficiency and versatility.
2Speed
If a specific easier-to-beat opponent is selected, then learning speed is improved, but the generalization capability deteriorates
Solution Approach 1:
The system implements feedback by evaluating the strength of each opponent before selection. This evaluation creates a feedback loop where the learning model's performance against evaluated opponents informs future opponent selection, balancing the need for achievable learning targets with the need to maintain generalization capability through diverse opponent exposure.
Solution Approach 2:
The patent introduces the competitive probability parameter that dynamically adjusts based on opponent strength evaluation. This parameter transformation allows the system to modulate the difficulty of opponents faced by the learning target, enabling controlled exposure to varying opponent strengths that promotes both learning speed and generalization.
3Adaptability or versatility
If multiple opponents with different strategies are used, then the versatility of the learning model is improved, but the complexity of the learning process increases
Solution Approach 1:
The system performs preliminary evaluation of opponent strengths before the actual learning process begins. This advance evaluation creates a prepared framework where multiple opponents are pre-assessed and assigned competitive probabilities, reducing the complexity during the learning execution phase while still maintaining versatility through diverse opponent selection.
Data Source
AI summary
There is provided a learning device including: a processing unit that performs reinforcement learning of a learning model of an agent under a competitive environment in which agents compete against each other, in which the learning model includes a hyperparameter, and the processing unit executes: a step of evaluating strengths of a plurality of the agents to be opponents of the agent as a learning target; a step of setting a competitive probability for the agent as the learning target according to the strength of the agent to be the opponent; a step of setting the agent to be the opponent based on the competitive probability; and a step of executing reinforcement learning of the agent as the learning target by causing the agent as the learning target to compete against the set agent to be the opponent.