Human-in-the-loop reinforcement learning: A survey and position on requirements, challenges, and opportunities
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …
enable agents to learn and perform tasks autonomously with superhuman performance …
A survey on explainable reinforcement learning: Concepts, algorithms, challenges
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
Symbols as a lingua franca for bridging human-ai chasm for explainable and advisable ai systems
Despite the surprising power of many modern AI systems that often learn their own
representations, there is significant discontent about their inscrutability and the attendant …
representations, there is significant discontent about their inscrutability and the attendant …
Safe driving via expert guided policy optimization
When learning common skills like driving, beginners usually have domain experts standing
by to ensure the safety of the learning process. We formulate such learning scheme under …
by to ensure the safety of the learning process. We formulate such learning scheme under …
[BOOK][B] Explainable human-AI interaction: A planning perspective
From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with
humans—swinging between their augmentation and replacement. Now, as AI technologies …
humans—swinging between their augmentation and replacement. Now, as AI technologies …
hmos: An extensible platform for task-oriented human–machine computing
With rapid advancements in artificial intelligence (AI) technologies, AI-powered machines
are increasingly capable of collaborating with humans to enhance decision-making in …
are increasingly capable of collaborating with humans to enhance decision-making in …
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning
Preference Based Reinforcement Learning has shown much promise for utilizing human
binary feedback on queried trajectory pairs to recover the underlying reward model of the …
binary feedback on queried trajectory pairs to recover the underlying reward model of the …
A survey on enhancing reinforcement learning in complex environments: Insights from human and llm feedback
AR Laleh, MN Ahmadabadi - arxiv preprint arxiv:2411.13410, 2024 - arxiv.org
Reinforcement learning (RL) is one of the active fields in machine learning, demonstrating
remarkable potential in tackling real-world challenges. Despite its promising prospects, this …
remarkable potential in tackling real-world challenges. Despite its promising prospects, this …
Data Driven Reward Initialization for Preference based Reinforcement Learning
M Verma, S Kambhampati - arxiv preprint arxiv:2302.08733, 2023 - arxiv.org
Preference-based Reinforcement Learning (PbRL) methods utilize binary feedback from the
human in the loop (HiL) over queried trajectory pairs to learn a reward model in an attempt …
human in the loop (HiL) over queried trajectory pairs to learn a reward model in an attempt …
Explainable and adaptable augmentation in knowledge attention network for multi-agent deep reinforcement learning systems
The scale of modem Artificial Intelligence systems has been growing and entering more
research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning …
research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning …