  Deep Q-Network (DQN) based multi-agent systems (MAS) for reinforcement
learning (RL) use various schemes where in the agents have to learn and
communicate. The learning is however specific to each agent and communication
may be satisfactorily designed for the agents. As more complex Deep QNetworks
come to the fore, the overall complexity of the multi-agent system increases
leading to issues like difficulty in training, need for higher resources and
more training time, difficulty in fine-tuning, etc. To address these issues we
propose a simple but efficient DQN based MAS for RL which uses shared state and
rewards, but agent-specific actions, for updation of the experience replay pool
of the DQNs, where each agent is a DQN. The benefits of the approach are
overall simplicity, faster convergence and better performance as compared to
conventional DQN based approaches. It should be noted that the method can be
extended to any DQN. As such we use simple DQN and DDQN (Double Q-learning)
respectively on three separate tasks i.e. Cartpole-v1 (OpenAI Gym environment)
, LunarLander-v2 (OpenAI Gym environment) and Maze Traversal (customized
environment). The proposed approach outperforms the baseline on these tasks by
decent margins respectively.
