While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Meta-reinforcement learning enables artificial agents to learn from related training tasks and
adapt to new tasks efficiently with minimal interaction data. However, most existing research …

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse
continuous control tasks has raised questions regarding performance degradation in …

Reinforcement Learning (RL) has become a critical tool for optimization challenges within
automation, leading to significant advancements in several areas. This review article …

Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves
multiple tasks at the same time. This paper presents a constrained formulation for multi-task …

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse
continuous control tasks has raised questions regarding performance degradation in …

Multi-agent reinforcement learning (MARL) has demonstrated significant potential in
enabling cooperative agents. The communication protocol, which is responsible for …

Agents often need a long time to explore state-action space in order to learn how to act
expectedly in Partially Observable Markov Decision Processes (POMDPs). With the reward …

Reinforcement learning (RL) aims to learn optimal behaviors for agents to maximize
cumulative rewards through trial-and-error interactions with dynamic environments. In recent …