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Interpreting 1 β(n+1) as a Lagrange multiplier, we have pα,β = arg min p∈{p∈P:R(p)=cα,β } Dα(p∥u) = arg min p∈H(cα,β) Dα(p∥u)

Z S pπ(s)Iα(s, pπ)dV (s) (55a) = R(π) − β(n + 1)Dα(p∥u) (55b) Then, the optima pα,β = arg max p∈P Rα,β(p) (56a) = arg max p∈P {R(p) − β(n + 1)Dα(p∥u)} (56b) = arg max p∈P −Dα(p∥u) + 1 β(n + 1)R(p) (56c) = arg min p∈P Dα(p∥u) − 1 β(n + 1)R(p · 2016

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An Information-Geometric Approach to Artificial Curiosity

cs.LG · 2025-04-08 · unverdicted · novelty 7.0

Information geometry constrains intrinsic rewards to strictly concave functions of reciprocal occupancy, with geodesic interpolation on the occupancy manifold yielding a scalar-parameter family that includes count-based and max-entropy exploration.

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  • An Information-Geometric Approach to Artificial Curiosity cs.LG · 2025-04-08 · unverdicted · none · ref 18

    Information geometry constrains intrinsic rewards to strictly concave functions of reciprocal occupancy, with geodesic interpolation on the occupancy manifold yielding a scalar-parameter family that includes count-based and max-entropy exploration.