Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

Reinforcement learning (RL) has achieved promising results in solving numerous
challenging sequential decision problems. To address the issue of sparse extrinsic rewards …

Policy optimization in reinforcement learning requires the selection of numerous
hyperparameters across different environments. Fixing them incorrectly may negatively …

Reinforcement learning (RL) has garnered significant attention for develo** decision-
making agents that aim to maximize rewards, specified by an external supervisor, within fully …

Одной из серьезных проблем в обучении с подкреплением являются редкие
вознаграждения от среды. Для решения этой задачи необходимы эффективные …

Transfer of recent advances in deep reinforcement learning to real-world applications is
hindered by high data demands and thus low efficiency and scalability. Through …

Discovering successful coordinated behaviors is a central challenge in Multi-Agent
Reinforcement Learning (MARL) since it requires exploring a joint action space that grows …

We propose discriminative reward co-training (DIRECT) as an extension to deep
reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL) …

The advent of reinforcement learning (RL) has significantly transformed decision making in
autonomous systems. However, its practical deployment faces substantial obstacles, chiefly …

Efficient exploration is a fundamental challenge in reinforcement learning, especially in
environments with sparse rewards. Intrinsic motivation can improve exploration efficiency by …