Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function ...
Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely...
(2021). Multi-agent reinforcement learning: A selective overview of theories and algorithms. Handbook of reinforcement learning and control, 321-384. Zhou, H., Lan, T., & Aggarwal, V. (2022). Pac: Assisted value factorization with counterfactual predictions in multi-agent reinforcement learning....
For a model-free DRL algorithms, we could have A2C algorithm, A3C algorithm(Mnih et al., 2016), PPO algorithm (Schulman et al., 2017), TRPO algorithm (Schulman et al., 2015)), Q-learning algorithm (Mnih et al., 2013), C51 algorithm (Bellemare et al., 2017), QR-DQN algorithm (...
Deep LearningExecution AlgorithmsReinforcement LearningOptimal ExecutionIn this article we introduce the term "Deep Execution" that utilize deep reinforcement learning (DRL) for optimal execution. We demonstrate two differdoi:10.2139/ssrn.3374766Dabérius, Kevin...
The joint action-value function (JAVF) plays a key role in the centralized training of multi-agent deep reinforcement learning (MADRL)-based algorithms using the value function decomposition (VFD) and in the generating process of a collaborative policy between agents. However, under the influence...