Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Amato, Christopher"'
Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. These attacks induce pre-determined, adversarial behavior in the agent upon observing a fixed trigger
Externí odkaz:
http://arxiv.org/abs/2410.13995
Autor:
Amato, Christopher
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many approaches have been developed but they can be divided into three main types: centralized training and execution (CTE), centralized training for decentralized
Externí odkaz:
http://arxiv.org/abs/2409.03052
Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full justification that may limit their performance. For example, the theory i
Externí odkaz:
http://arxiv.org/abs/2408.15381
Publikováno v:
Journal of Artificial Intelligence Research 77 (2023): 295-354
Centralized Training for Decentralized Execution where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approach in Multi-Agent Reinforcement Learning (MARL). In particular, it has
Externí odkaz:
http://arxiv.org/abs/2408.14597
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient l
Externí odkaz:
http://arxiv.org/abs/2408.14336
Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness of RL algorithms against adversarial attacks. In this work we explore
Externí odkaz:
http://arxiv.org/abs/2405.20539
Autor:
Amato, Christopher
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many approaches have been developed but they can be divided into three main types: centralized training and execution (CTE), centralized training for decentralized
Externí odkaz:
http://arxiv.org/abs/2405.06161
Navigating in unseen environments is crucial for mobile robots. Enhancing them with the ability to follow instructions in natural language will further improve navigation efficiency in unseen cases. However, state-of-the-art (SOTA) vision-and-languag
Externí odkaz:
http://arxiv.org/abs/2310.10822
Traffic signal control (TSC) is a challenging problem within intelligent transportation systems and has been tackled using multi-agent reinforcement learning (MARL). While centralized approaches are often infeasible for large-scale TSC problems, dece
Externí odkaz:
http://arxiv.org/abs/2310.02435
Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expert information on robot dynamics. Bayesian reinforcement learning (BRL
Externí odkaz:
http://arxiv.org/abs/2307.11954