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Found 6 papers, 1 papers with code
Sparse and Structured Hopfield Networks
Modern Hopfield networks have enjoyed recent interest due to their connection to attention in transformers.
Causal policy ranking
Policies trained via reinforcement learning (RL) are often very complex even for simple tasks.
Hierarchical model-based policy optimization: from actions to action sequences and back
We develop a normative framework for hierarchical model-based policy optimization based on applying second-order methods in the space of all possible state-action paths.
Policy path programming
We develop a normative theory of hierarchical model-based policy optimization for Markov decision processes resulting in a full-depth, full-width policy iteration algorithm.
Characterizing optimal hierarchical policy inference on graphs via non-equilibrium thermodynamics
Hierarchies are of fundamental interest in both stochastic optimal control and biological control due to their facilitation of a range of desirable computational traits in a control algorithm and the possibility that they may form a core principle of sensorimotor and cognitive control systems.
Efficient state-space modularization for planning: theory, behavioral and neural signatures
Even in state-spaces of modest size, planning is plagued by the “curse of dimensionality”.
