lucalumetti/dqnts

Implementation of Tabu Search guided by a Deep Q-Network

DQNTS: Deep Q-Learning Tabu Search

Introduction

In this project, I've tried to face the Max-Mean Dispersion Problem using Tabu Search guided by a deep reinforcement learning algorithm during the dispersion phase. Given a complete graph G(V, E) where each edge has associated a distance or affinity $d_{ij}$, the Max Mean Dispersion Problem is the search of a subset of vertex $M \subset V, |M| >= 2$ which maximize the mean dispersion, calculated as follow:

$$ MeanDispersion(M) = \frac{\sum_{i,j \in M}d_{ij}}{|M|} $$

This problem is known to be strongly NP-hard and has the characteristic that the subset $M$ does not have a fixed size, but can vary between $2$ and $|V|$.

RLTS

In 2020, Nijimbere et al. proposed an approach based on the combination of reinforcement learning and tabu search, named RLTS. The main idea is to use Q-Learning to build a high-quality initial solution, then improving it with a one-flip tabu search. Then Q-Learning algorithm is trained during the search of the solution, by giving a positive or negative reward if the node remain or not in the solution after the tabu search.

DQNTS

My proposed solution is to use a Deep Q-Network to generate the initial solution and tabu search to improve it, hence the name DQNTS. The approach is similar to \cite{nijimbere2020tabu}, the network learns a heuristic to build the initial solution, then uses a one-flip tabu search to improve that solution. The difference is that the DQN is pre-trained, hence there is no need to start with a random solution and I can generate a high-quality initial solution from the beginning.

Network Architecture

The network architecture is based on "Learning combinatorial optimization algorithms over graphs" by Dai et al. The state2tens embedding is done with 4 features extracted from each node, which are:

• 1 if the node in the solution, 0 otherwise
• the sum of all edges connected to the node
• the sum of all edges connected to the node and the solution nodes
• the sum of all edges connected to the node and the nodes not in the solution

Network training

To train the network, first, we construct a feasible solution using an $\epsilon$-greedy strategy, stopping when no positive rewards are predicted by the network. Then the solution is given to the one-flip tabu search which improves it and returns the best solution. For every node in the initial solution which remains in the final solution after tabu search, a reward of +1 is given, otherwise, the reward is -1. The network has been trained over 10 different instances (MDPIx_35 and MDPIIx_35, $1 <= x <= 10$) for 5001 episodes.

Tabu Search

The tabu search implementation is the same as in \cite{nijimbere2020tabu}, with the only difference in the parameter $\alpha = 100$ instead of $\alpha = 50000$. My implementation couldn't finish even a single iteration in the time limit imposed with the parameter $\alpha$ proposed in the paper. This made me think that my implementation is way slower, but still, I left the same time constraints as the results were good enough.

General Algorithm

DQN is used during the construction of the initial solution: for all the nodes, the network estimates the reward, then all the values estimated get interpolated in the range $[-1,+1]$. All the nodes with a value $>= 0$ are named as "good nodes". Among these "good nodes", a random amount is taken to construct the initial solution. Picking one node at a time would result in a better solution, but this approach was too slow for large graphs. This solution is then processed with a one-flip tabu search until no best solutions are found for $\alpha = 100$ iterations in a row. Now a new initial solution is generated and the process is repeated until the time limit is not violated. Finally, the best solution found is returned.

Result

Results can be seen on the pdf presentation uploaded in this repository under the pdf directory.