We can compute the fixed point of the recursion in Equation A.2 and get the following estimated Then we compare these two gaps. To utilize the Eq. 4 for policy optimization, following the analysis in the Section 3.2 in Kumar et al. By choosing different regularizer, there are a variety of instances within CQL family. B.36 called CFCQL( H) which is the update rule we used: In discrete action space, we train a three-level MLP network with MLE loss. In continuous action space, we use the method of explicit estimation of behavior density in Wu et al.

In fact, the interaction of these two aspects requires addressing the fact that each agent's own safety constraint requires information from all others.

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.

This section consists of three parts, with each subsequent part building upon the previous one. Appendix A.1 covers the fundamentals of RL, where the actor-critic method is introduced. Appendix A.2 describes the RL algorithm for a single fulfillment agent, which is the proximal policy Appendix A.3 presents the MARL algorithm for the Currently, policy-based methods [Deisenroth et al., 2013] are prevalent because they are compatible with stochastic To sum up, the complete procedure is given in Algorithm 1.Algorithm 1 Heterogeneous Multi-Agent Reinforcement Learning for Order Fulfillment. With regard to the advantage estimator, we set the GAE parameters [Schulman et al., 2016] To highlight how our proposed benchmark differs from existing approaches focused on sub-tasks of order fulfillment, we compare the objectives, observations, and actions in Table 1. It should be noted that multiple formulations exist for each sub-task.

Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). To obtain meaningful information for decision-making, previous works typically combine raw messages generated by teammates with local information as inputs for policy. However, neglecting the aggregation of multiple messages poses great inefficiency for policy learning.