A.1 Full derivation425 We present the complete derivation of the objective function in each subproblem defined in Section426 3.2. For brevity, let rt =(1+)rEt +rIt and V EE (st)= Vt. Under this assumption, E serves as 0 (see above). This451 enables updating E+I using the local approximation. We leave relaxing this assumption as future452 work.453

Recent developments in reinforcement learning (RL) led to algorithms that surpass human experts in a broad range of tasks [Mnih et al., 2015, Vinyals et al., 2019, Schrittwieser et al., 2020, Wurman et al.,

The ability to approach the same problem from different angles is a cornerstone of human intelligence that leads to robust solutions and effective adaptation to problem variations. In contrast, current RL methodologies tend to lead to policies that settle on a single solution to a given problem, making them brittle to problem variations. Replicating human flexibility in reinforcement learning agents is the challenge that we explore in this work.

Whether itispossible tolearn intrinsic reward functions for learning agents remains an open problem.

Conversational Recommender Systems (CRS) actively elicit user preferences to generate adaptive recommendations. Mainstream reinforcement learning-based CRS solutions heavily rely on handcrafted reward functions, which may not be aligned with user intent in CRS tasks.

Much of this effort has focused on the single-agent setting, in which an agent maximizes a predefined extrinsic reward function.