3 accepted papers
Deep reinforcement learning (DRL) methods can solve complex continuous control tasks in simulated environments by taking actions based solely on state observations at each decision point. Because of the dynamics involved, individual snapshots of real-world sensor measurements afford only partial sta…
Deep Reinforcement Learning (DRL) faces challenges bridging the sim-to-real gap to enable real-world applications. In contrast to the simulated environments used in conventional DRL training, real-world systems are non-linear and evolve in an asynchronous fashion; sensors and actuators have limited…
The Shapley value is a well recognised method for dividing the value of joint effort in cooperative games. However, computing the Shapley value is known to be computationally hard, so stratified sample-based estimation is sometimes used. For this task, we provide two contributions to the state of th…