Memoryless policies: theoretical limitations and practical results
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot learning driven by emotions
Adaptive Behavior
FLAME—Fuzzy Logic Adaptive Model of Emotions
Autonomous Agents and Multi-Agent Systems
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective
IEEE Transactions on Autonomous Mental Development
Genetic Programming for Reward Function Search
IEEE Transactions on Autonomous Mental Development
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In this paper, we propose an adaptation of four common appraisal dimensions that evaluate the relation of an agent with its environment into reward features within an intrinsically motivated reinforcement learning framework. We show that, by optimizing the relative weights of such features for a given environment, the agents attain a greater degree of fitness while overcoming some of their perceptual limitations. This optimization process resembles the evolutionary adaptive process that living organisms are subject to. We illustrate the application of our method in several simulated foraging scenarios.