RL Routing

Overview

The Anchor Protocol uses a reinforcement learning (RL) algorithm for collaborative networking. It automatically balances exploration of new paths with exploitation of established ones. New paths are formed along the chain stream → route → tether; existing paths are reused when they perform well. The result is a network that continuously adapts and self-recovers, offering guaranteed connectivity at all times—well suited to scale-elastic workloads.

Exploration and exploitation

Anchor’s RL logic decides when to explore a new path (discover and form a new stream, route, and tether) and when to exploit paths that are already working. Exploration improves the set of available routes and helps the overlay recover when conditions change; exploitation keeps traffic on known-good paths and reduces overhead. The balance between the two is driven by observed outcomes, so the network responds to real conditions rather than static configuration.

Collaborative operation over the control channel

The algorithm runs in collaboration with other nodes over the control channel. Each node senses network conditions (latency, loss, throughput, reachability) and exchanges information via the control plane. RL decisions—when to try a new path, when to stick with an existing one—are made using this shared view, so the overlay behaves as a single adaptive system rather than a set of independent routers. There is no central route controller; coordination happens through the same control channel that carries other Anchor state.

Self-adapting, guaranteed connectivity

Because routing is driven by RL over the control channel, the network is always adapting and self-recovering. When a path degrades or fails, the exploration side of the algorithm discovers alternatives; when paths are good, exploitation keeps traffic on them. Connectivity is guaranteed: the overlay keeps flows working by continuously adapting paths. Topology and routes may change as the network self-heals, but the goal is reliable, sustained connectivity. This makes Anchor a good fit for scale-elastic workloads—services that grow and shrink, migrate, or run in dynamic environments where paths and peers change frequently and the goal is robust, adaptive connectivity rather than a static mesh.