reviews: 
Comments: This paper proposed a switching mechanism to cooridnate exploration behaviors from two agents, an SAC agent with curiosity-based exploration strategy, and a co-behavior agent with only stochastic policy to draw actions. The proposed algorithm used the novelty intrinsic reward to choose which agent to output action by comaparing with a threshold. Therefore, the proposed algorithm can actively corredinate two different exploration strategies.
Strengths: The approach presented in this paper is straightforward and easy to implement. It effectively combines two exploration strategies (curiosity-driven exploration and stochastic policy sampling). Experimental results demonstrate improved performance compared to approaches that rely on a single exploration strategy.
Weaknesses: 1. The underlying assumption for the switching criterion is not intuitive. In Equation (4) and the following explanation (from around Line 239 to Line 245), it is stated that for low intrinsic reward regions (i.e., low novelty), KEA switches to curiosity-based exploration to encourage the agent to explore novel areas. Conversely, for high novelty regions, KEA switches to a more stochastic sampling policy. However, it would make sense if it was the other way around, as high intrinsic rewards usually indicate that the area is novel and worth exploring further, making it more logical to apply a strong exploration strategy. Conversely, low intrinsic reward regions (low novelty) might be better suited for stochastic sampling. I recommend that this assumption be supported with more quantitative analysis or additional comparative experiments.

2. The paper conflates curiosity-based exploration and novelty-based intrinsic reward exploration. They are generally considered two distinct approaches for encouraging exploration. While this doesn't undermine the core idea of the paper, clearer distinctions between the two should be made, and the terminology used should be more precise. Including more related representative works would also help.

3. The experimental section can be improved. First, I suggest that the authors include additional baselines that focus specifically on exploration, beyond just the backbone algorithms. Since KEA’s core idea is to combine two exploration strategies, only comparing it against SAC, RND, and NovelD seems more like an ablation study (with only one agent in KEA involved). Representative SOTA works like [1][2][3] would offer better context for evaluating KEA’s performance. Additionally, for KEA itself, it would be valuable to explore other factors, such as how different values for the switching criterion (hyperparameter $\delta$) affect the model's switching behavior, how many times were $\pi^{SAC}$ and $\pi^B$ activated in training, etc.

4. The writing and expression can be improved, for example: (1) Lines 199-203 and Lines 128-130 are repeated verbatim, and (2) paragraphs like Lines 308-315 and Lines 404-411 could be better presented in table format for clearer understanding.

[1] Devidze, Rati, Parameswaran Kamalaruban, and Adish Singla. "Exploration-guided reward shaping for reinforcement learning under sparse rewards." Advances in Neural Information Processing Systems (2022).

[2] Trott, Alexander, et al. "Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards." Advances in Neural Information Processing Systems (2019).

[3] Hong, Zhang-Wei, et al. "Diversity-driven exploration strategy for deep reinforcement learning." Advances in neural information processing systems (2018).
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2