Generalized proximal policy optimization with sample reuse
J Queeney, Y Paschalidis… - Advances in Neural …, 2021 - proceedings.neurips.cc
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 …
methods to be both stable and sample efficient. On-policy methods typically generate …
A novel policy based on action confidence limit to improve exploration efficiency in reinforcement learning
F Huang, X Deng, Y He, W Jiang - Information Sciences, 2023 - Elsevier
Reinforcement learning has been used to solve many intelligent decision-making problems.
However, reinforcement learning still faces a challenge of the low exploration efficiency …
However, reinforcement learning still faces a challenge of the low exploration efficiency …
A modified random network distillation algorithm and its application in USVs naval battle simulation
J Rao, X Xu, H Bian, J Chen, Y Wang, J Lei… - Ocean …, 2022 - Elsevier
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 …
profoundly, and one of the critical factors for victory is the cluster intelligence of USVs …
A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error
Traditional backward recursion methods face a fundamental challenge in solving Markov
Decision Processes (MDP), where there exists a contradiction between the need for …
Decision Processes (MDP), where there exists a contradiction between the need for …
Explanation-aware experience replay in rule-dense environments
Human environments are often regulated by explicit and complex rulesets. Integrating
Reinforcement Learning (RL) agents into such environments motivates the development of …
Reinforcement Learning (RL) agents into such environments motivates the development of …
Mixed experience sampling for off-policy reinforcement learning
J Yu, J Li, S Lü, S Han - Expert Systems with Applications, 2024 - Elsevier
In deep reinforcement learning, experience replay is usually used to improve data efficiency
and alleviate experience forgetting. However, online reinforcement learning is often …
and alleviate experience forgetting. However, online reinforcement learning is often …
Lucid dreaming for experience replay: refreshing past states with the current policy
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 …
algorithms by allowing an agent to store and reuse its past experiences in a replay buffer …
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation
A Andres, E Villar-Rodriguez… - 2022 IEEE Symposium …, 2022 - ieeexplore.ieee.org
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 …
efficiently. The use of these algorithms highly depends on the feedback signals provided by …
Elements of episodic memory: insights from artificial agents
A Boyle, A Blomkvist - … Transactions of the Royal Society B …, 2024 - eprints.lse.ac.uk
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 …
these 'episodic-inspired'AI systems might inform our understanding of biological episodic …
An effective maximum entropy exploration approach for deceptive game in reinforcement learning
C Li, X Wei, Y Zhao, X Geng - Neurocomputing, 2020 - Elsevier
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 …
the global optimization and have been grown up to become a huge challenge in the field of …