In real-world decision making tasks, it is critical for data-driven reinforcement learning
methods to be both stable and sample efficient. On-policy methods typically generate …

Reinforcement learning has been used to solve many intelligent decision-making problems.
However, reinforcement learning still faces a challenge of the low exploration efficiency …

Unmanned surface vessel (USV) operations will change the future form of maritime wars
profoundly, and one of the critical factors for victory is the cluster intelligence of USVs …

Traditional backward recursion methods face a fundamental challenge in solving Markov
Decision Processes (MDP), where there exists a contradiction between the need for …

Human environments are often regulated by explicit and complex rulesets. Integrating
Reinforcement Learning (RL) agents into such environments motivates the development of …

In deep reinforcement learning, experience replay is usually used to improve data efficiency
and alleviate experience forgetting. However, online reinforcement learning is often …

Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL)
algorithms by allowing an agent to store and reuse its past experiences in a replay buffer …

Reinforcement Learning has emerged as a strong alternative to solve optimization tasks
efficiently. The use of these algorithms highly depends on the feedback signals provided by …

Many recent AI systems take inspiration from biological episodic memory. Here, we ask how
these 'episodic-inspired'AI systems might inform our understanding of biological episodic …

Deceptive games are games that utilize the reward structure to keep the agent away from
the global optimization and have been grown up to become a huge challenge in the field of …