… a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic
robots with continuous control in an unknown dynamic environment with moving obstacles. …

… of the reinforcement learning techniques for tackling dynamically changing environment
contexts in a system. The focus is on a single autonomous RL agent learning a sequence of …

… Beyond that, we consider continual learning (also known as lifelong learning) environments…
within episodes in non-stationary environments and continual learning settings, we aim to …

… ) in dynamic environments with potential threats. In this paper, we have proposed a Deep
Reinforcement Learning (… to provide the simulation environment where a situation assessment …

… This paper explores how to apply reinforcement learning to more complex real world and …
, to improve the reward estimation in dynamic environments. Then, we propose Robust DQN by …

… The experimental results verified that the proposed method improved the performance of
obstacle avoidance for multiple dynamic environments without any additional work. It has been …

… existing learning-based methods, we introduce a globally guided reinforcement learning …
a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the …

… to reinforcement learning RL algorithms in order to successfully interact within dynamic
environments… improve the learning times of RL agents in a dynamic environment. However, direct …

… problems in high dynamic environments. Numerous drifting … collision with the high dynamic
obstacles causes lots of … of positive episodes for DRL to learn the desired policy. This …

… to navigate in a complex dynamic environment is one of the most attractive tasks. It is also …
in the dynamic environment with rapidly moving obstacles with deep reinforcement learning. …