EvoSK couples reinforcement learning with evolutionary replacement on a rugged landscape to self-organize at the edge of ergodicity breaking, yielding scale-free avalanches with exponent near -1.5 and superior rewards.
Across all realizations, these distri- butions stabilize after a transient time oft stat = 15,000 iterations
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Self-Organization to the Edge of Ergodicity Breaking in a Complex Adaptive System
EvoSK couples reinforcement learning with evolutionary replacement on a rugged landscape to self-organize at the edge of ergodicity breaking, yielding scale-free avalanches with exponent near -1.5 and superior rewards.