New Bellman Equation Targets Plasticity in Reinforcement Learning

Researchers have introduced a new Bellman optimality equation specifically designed to optimize plasticity in continual reinforcement learning. This work builds upon a prior formalization that reframed the stability-plasticity tradeoff as an empowerment-plasticity tradeoff, where plasticity is defined by the directed information from observations to actions, and empowerment by the directed information from actions to observations. While empowerment has been extensively studied, this paper marks the first attempt to address the optimization of plasticity under this new definition within Markov decision processes. AI

IMPACT Introduces a novel mathematical framework for optimizing plasticity in continual reinforcement learning, potentially advancing agent adaptability.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and equation for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Abel et al.
• Bellman optimality equation
• directed information
• empowerment
• Markov decision processes
• plasticity
• reinforcement learning

• paper
• other

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