NEO is a probabilistic neural model that induces compositional programs as a learned Language of Thought from non-textual observations and executes them via a shared transition model to enable explanation-driven generalization.
arXiv preprint arXiv:1705.02670 , Title =
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Trains a gating policy to select state-dependent planning budgets in variable-delay real-time RL, outperforming fixed-budget and heuristic baselines across Pac-Man, Tetris, Snake, Speed Hex, and Speed Go.
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Learning to Theorize the World from Observation
NEO is a probabilistic neural model that induces compositional programs as a learned Language of Thought from non-textual observations and executes them via a shared transition model to enable explanation-driven generalization.
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Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Trains a gating policy to select state-dependent planning budgets in variable-delay real-time RL, outperforming fixed-budget and heuristic baselines across Pac-Man, Tetris, Snake, Speed Hex, and Speed Go.