Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning
نویسندگان
چکیده
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores and reuses complete Q-tables for continuous problem solving. We demonstrate CBRetaliate on a team-based first-person shooter game, where our combined CBR+RL approach adapts quicker to changing tactics by an opponent than standalone RL.
منابع مشابه
Combining Case-Based Reasoning and Reinforcement Learning for Unit Navigation in Real-Time Strategy Game AI
This paper presents a navigation component based on a hybrid case-based reasoning (CBR) and reinforcement learning (RL) approach for an AI agent in a real-time strategy (RTS) game. Spatial environment information is abstracted into a number of influence maps. These influence maps are then combined into cases that are managed by the CBR component. RL is used to update the case solutions which ar...
متن کاملCombining Case-Based Reasoning and Reinforcement Learning for Tactical Unit Selection in Real-Time Strategy Game AI
This paper presents a hierarchical approach to the problems inherent in parts of real-time strategy games. The overall game is decomposed into a hierarchy of sub-problems and an architecture is created that addresses a significant number of these through interconnected machinelearning (ML) techniques. Specifically, individual modules that use a combination of case-based reasoning (CBR) and rein...
متن کاملCase-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case-Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Acce...
متن کاملThe Use of Cases as Heuristics to speed up Multiagent Reinforcement Learning
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Acce...
متن کاملBidding Strategy on Demand Side Using Eligibility Traces Algorithm
Restructuring in the power industry is followed by splitting different parts and creating a competition between purchasing and selling sections. As a consequence, through an active participation in the energy market, the service provider companies and large consumers create a context for overcoming the problems resulted from lack of demand side participation in the market. The most prominent ch...
متن کامل