Multi agent systems have always successfully provided a platform to solve problems. Using reinforcement learning, agents can learn to understand their environment and thus deal with complex problems through optimisation. However, there is one major challenge: agents always try to maximize their own reward, which means that they do not take other entities into consideration and as a result act selfish. This raises the question of how to overcome this problem and enable cooperation between agents. One approach to create cooperation is action trading, which allows agents to trade with each other. This concept has already been demonstrated in a few domains, namely the Iterated Matrix Game and the Coin Game. In this work we will try to expand the approach to new environments and to take a more in-depth analysis of the trading amount. As a result, we should gain more insight into the cooperative behavior of agents and possibly improve the trading mechanism.
