While positive reward prediction errors (RPEs) and negative RPEs have equal impacts in the standard reinforcement learning, the brain appears to have distinct neural pathways for learning mainly from either positive or negative feedbacks, such as the direct and indirect pathways of the basal ganglia (BG). Given that distinct pathways may receive inputs unevenly from different neural populations and/or regions, how states or actions are represented can differ between the pathways. We explored whether combined use of different representations, coupled with different learning rates from positive and negative RPEs, has computational benefits. We considered an agent equipped with two learning systems, each of which adopted individual representation (IR) or successor representation (SR) of states. With varying the combination of IR or SR and also the learning rates from positive and negative RPEs in each system, we examined how the agent performed in a certain dynamic reward environment. We found that combination of SR-based system learning mainly from positive RPEs and IR-based system learning mainly from negative RPEs outperformed the other combinations, including IR-only or SR-only cases and the cases where the two systems had the same ratios of positive- and negative-RPE-based learning rates. In the best combination case, both systems show activities of comparable magnitudes with opposite signs, consistent with suggested profiles of BG pathways. These results suggest that particularly combining different representations with appetitive and aversive learning could be an effective learning strategy in a certain dynamic reward environment, and it might actually be implemented in the cortico-BG circuits.