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Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

The authors examine the effect of climate risk on accounting conservatism for a sample of listed companies operating in 26 developing countries.

The authors employ the Climate Risk Index (CRI) developed by Germanwatch to capture the severity of losses due to extreme weather events at the country level. The authors use different approaches to measure firm-level accounting conservatism.

The authors find that greater climate risk leads to a lower level of accounting conservatism. The results hold even after using different estimation methods.

Although the authors' analysis is limited to the period 2007–2016, it could be helpful for standard setters such as International Accounting Standards Board (IASB) and International Sustainable Standards Board (ISSB) as they may consider the potential effect of climate risk in their international standards.

The negative impacts of climate risk on the quality of financial reporting as proxied by accounting conservatism could trigger regulators and standard setters to require disclosure of information relating to climate risks and to incorporate climate-related risks in their risk management systems. In addition, for policymakers, incorporating accounting conservatism as a financial quality reporting standard could help promote greater transparency, accuracy and reliability in financial reporting in the context of climate risk.

The authors add to the literature on international differences in accounting conservatism by showing that climate risk significantly affects unconditional and conditional conservatism. The authors' results provide fresh evidence of the dark side of climate change. That is, climate risk is shown to decrease financial reporting quality.