Bipedal robots are garnering increasing global attention due to their potential applications
and advancements in artificial intelligence, particularly in Deep Reinforcement Learning …

This paper introduces a novel multi-agent Deep Reinforcement Learning (DRL) framework
named the Cooperative Dual-Actor Proximal Policy Optimization (CDA-PPO) algorithm …

The upset state is an unexpected flight state, which is characterized by an unintentional
deviation from normal operating parameters. It is difficult for the pilot to recover the aircraft …

Neural network (NN) control systems face significant challenges in the theoretical
examination of closed-loop stability, despite their success. This paper presents a …

Recently, the field of robotics development and control has been advancing rapidly. Even
though humans effortlessly manipulate everyday objects, enabling robots to interact with …

This study proposes a novel hybrid learning approach for developing a visual path-following
algorithm for industrial robots. The process involves three steps: data collection from a …

Multi-Agent Reinforcement Learning (MARL) faces formidable challenges when tackling
cooperative tasks due to the expansive state space. Traditional approaches, such as …

Single-agent and multi-agent systems are integral to the dynamic environmental processes
of reinforcement learning in advanced humanoid robotic applications. This thesis introduces …

Триботронные опорные узлы представляют собой мультифизическую систему,
основанную на совокупности гидродинамических, теплофизических, динамических …