Implement Q-learning routing protocol

[Copilot]

Adds reinforcement learning-based routing using Q-learning algorithm. Nodes learn optimal routes through experience rather than predefined metrics.

Core Protocol (src/inet/routing/qlearning/)

• QLearn.{h,cc,ned}: Main protocol implementation with Q-table for (destination, next_hop) → quality mappings
• QLearningPacket.msg: HELLO (17B) and UPDATE (21B) message definitions
• QLearningPacketSerializer.{h,cc}: Network serialization
• Q-value updates via: Q(s,a) ← Q(s,a) + α[R + γ·max(Q(s',a')) - Q(s,a)]
• Epsilon-greedy route selection (exploration vs exploitation)
• Periodic neighbor discovery via HELLO broadcasts

Node & Examples

• QLearningRouter.ned: AdhocHost extension with integrated Q-learning
• examples/qlearning/: Three test scenarios (static, mobile, 3-node)
• tests/module/QLearningSimpleTest.test: Basic functionality verification

Configuration

Tunable parameters:

• Learning: learningRate (α=0.5), discountFactor (γ=0.9), explorationRate (ε=0.1)
• Protocol: helloInterval (2s), routeTimeout (10s), qUpdateInterval (5s)
• Rewards: successReward (+1.0), failurePenalty (-1.0), hopPenalty (-0.1/hop)

Usage:

network MyNetwork {
    submodules:
        node[10]: QLearningRouter {
            qlearn.learningRate = 0.5;
            qlearn.explorationRate = 0.1;
        }
}

Build Integration

• Added QLearning and QLearningExamples features to .oppfeatures
• Statistics: packets sent/received/forwarded/dropped, Q-table size, neighbor count

Original prompt

I want to implement q-learning routing

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