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Benchmarking projective simulation in navigation problems
Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory.
Meta-learning within Projective Simulation
The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.
Projective simulation with generalization
Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.
Projective simulation applied to the grid-world and the mountain-car problem
We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.
Projective simulation for classical learning agents: a comprehensive investigation
We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced [H. J.
