A Demonstration of Agent Learning with Action-Dependent Learning Rates in Computer Role-Playing Games
نویسندگان
چکیده
We demonstrate combat scenarios between two NPCs in the Neverwinter Nights (NWN) game in which an NPC uses a new learning algorithm ALeRT (Action-dependent Learning Rates with Trends) and the other NPC uses a static strategy (NWN default and optimal) or a dynamic strategy (dynamic scripting). We implemented the ALeRT algorithm in NWScript, a scripting language used by NWN, with the goal to improve the behaviours of game agents. We show how our agent learns and adapts to changes in the environment.
منابع مشابه
Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games
We introduce the ALeRT (Action-dependent Learning Rates with Trends) algorithm that makes two modifications to the learning rate and one change to the exploration rate of traditional reinforcement learning techniques. Our learning rates are action-dependent and increase or decrease based on trends in reward sequences. Our exploration rate decreases when the agent is learning successfully and in...
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