Before revision:  We show that our CRL architecture is able to both find optimal solutions in discrete and continuous time and reproduce human experimental data on standard game-theoretic metrics such as efficiency in acquiring rewards , fairness in reward distribution and stability of convention formation .
After revision:  We show that our CRL architecture is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards , fairness in reward distribution and stability of convention formation .