Zack Dulberg

This paper shows that separate pain and pleasure processing systems outperform unified reward systems in dynamic environments through modular reinforcement learning architectures.

This paper uses reinforcement learning with memory replay to explain the normative value of grief in terms of reward maximization.

This paper shows that modular agents, composed of subagents for distinct needs, outperform monolithic agents in maintaining homeostasis in changing environments.