reviews: 
Comments: In this paper, the authors present the first model-based solution, DLPA, for the RL problem in PAMDP settings where both discrete and continuous actions coexist. DLPA initially learns an environment model that accommodates both discrete and continuous actions. It optimizes this model using prediction errors over a continuous H-step horizon when the state input includes only the true state at the first step. Subsequently, DLPA employs the CEM method extended to the PAMDP setting, based on this model, to perform MPC.
Strengths: 1. This paper introduces a model-based approach for the first time in the context of PAMDP settings and addresses the learning challenges posed by the large action space through the application of CEM-based MPC methods.
2. The experimental results showcase the superior performance of the approach presented in this paper in terms of sample efficiency and final performance.
Weaknesses: 1. Quite a few grammar errors：
    - Sec 1, first paragraph: “… at each time step the agent must choose a discrete action type (move, ribble or shoot) and also continuous parameters (related to) that chosen discrete action, … each discrete action is parameterized by some continuous parameters, …”.
    - Sec 1, second paragraph: “By contrast, in continuous/discrete-only action spaces, …”.
    - Sec 1, third paragraph: ”It then performs Model Predictive Control …”.
    - Sec 4.2, third paragraph: “The discrete actions and continuous parameters …”.
    - Sec 4.2, fourth paragraph: “This is because each time, the agent needs to first sample from the discrete actions then sample from the corresponding continuous parameters. And each discrete action has a independent continuous parameter space. This becomes …”.
    - Sec 4.2, fifth paragraph: “… the agent executes E steps of forward planning while updating the distribution parameters over discrete actions and continuous parameters. Then it uses the first action …”.
    - Sec 5, first paragraph: “Goal: The agent needs …”.
    - Sec 5, first paragraph: “The number of parameterized actions is …”.
    - Sec 5, second paragraph: “HyAR learns an embedding of the parameterized action space …”.
    - Sec 5.1, first paragraph: “We find that DLPA achieves significantly higher sample efficiency …, … which is consistent with the results …, … DLPA on average achieves 30× higher sample efficiency compared to the best model-free method …”.
2. The author's description of the key innovations in this paper, such as the calculation of the H-step loss in model learning or the introduction of the CEM method, does not place enough emphasis on what makes these approaches unique when applied to the specific problem scenario PAMDP. For instance, what sets apart the H-step loss in DLPA compared to the traditional model-based approaches, or how does this paper's use of CEM bring innovation in the context of PAMDP, especially regarding handling the relationship between discrete and continuous actions? These are the questions that are more central to the paper and that I would like to have a better understanding of.
3. During the experimental process, the number of training steps for DLPA differs from the other baseline methods. This discrepancy makes it challenging to adequately assess the stability of DLPA's training, including whether it exhibits oscillations or other issues. Additionally, it appears that HyAR may not achieve full convergence in certain environments. Therefore, it would be advisable to provide additional experimental results with a consistent number of training steps for all algorithms to ensure a fair comparison.
4. For the experiments assessing the impact of CEM-based MPC on DLPA, it would be beneficial to include ablation studies with other traditional model-based non-MPC methods, such as MBPO, applied in the context of PAMDP.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 2 fair