  Traditional multi-agent reinforcement learning (MARL) algorithms, such as
independent Q-learning, struggle when presented with partially observable
scenarios, and where agents are required to develop delicate action sequences.
This is often the result of the reward for a good action only being available
after other agents have taken theirs, and these actions are not credited
accordingly. Recurrent neural networks have proven to be a viable solution
strategy for solving these types of problems, resulting in significant
performance increase when compared to other methods. In this paper, we explore
a different approach and focus on the experiences used to update the
action-value functions of each agent. We introduce the concept of
credit-cognisant rewards (CCRs), which allows an agent to perceive the effect
its actions had on the environment as well as on its co-agents. We show that by
manipulating these experiences and constructing the reward contained within
them to include the rewards received by all the agents within the same action
sequence, we are able to improve significantly on the performance of
independent deep Q-learning as well as deep recurrent Q-learning. We evaluate
and test the performance of CCRs when applied to deep reinforcement learning
techniques at the hands of a simplified version of the popular card game
Hanabi.
