SlowGuess/NeurIPS_2023_OCR huggingface dataset:
One key highlight of this paper is the effectiveness of incorporating both methods rather than solely focusing on a single aspect through an elegant algorithm. To support our viewpoint, we evaluate different exploration strategies on the baselines [27, 32] and present the results on the MACO benchmark in Fig. 8 and Fig 9. In the figure, the dashed line represents solely applying the curiosity-based intrinsic reward, while the dotted line represents solely applying the influence-based intrinsic reward. It is worth noting that either exploration strategy may fail in certain scenarios. However, by incorporating both methods, we consistently achieve the best results. We also perform experiments on the StarCraft II benchmark with sparse reward setting, and the results can be shown in Fig. 10, in which MASER [10] is a goal based exploration method.
(a) Aloha
Figure 8: Performance of different kinds of exploration strategies on MACO benchmark.
(c) Hallway
Figure 9: Ablations of our method when applying COIN to QPLEX.
Figure 10: Results on StarCraft II sparse setting with map  $3\mathrm{m(left)}$  and  $2\mathrm{s}3\mathrm{z(right)}$ , where the rewards only come from death, killed or terminated.