International Foundation for Autonomous Agents and MultiAgent Systems ( IFAAMAS )

2007

Subfields: LLM Agents & Tool Use, Reinforcement Learning

Citations in the corpus

• Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2020, background): “Palmer et al. (2019) maintain a learned interval whose lower bound is approximately the minimum cumulative reward received when all agents are coordinating.”
• Deal or No Deal? End-to-End Learning of Negotiation Dialogues (2017, background): “The only automated natural language negotiations systems we are aware of have first mapped language to domain-specific logical forms, and then focused on choosing the next dialogue act (Rosenfeld et al., 2014; Cuay´ahuitl et al., 2015; Keizer et al., 2017).”
• Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning (2022, methodology): “Song et al. (2016) propose a metric between MDPs using the Kantorovich and Hausdorff metrics and use it for transferring value functions between source tasks and a target task.”
• Observational Overfitting in Reinforcement Learning (2019, background): “Furthermore, various hyperparameters such as the batch-size in SGD (Smith et al., 2018), choice of optimizer (Kingma & Ba, 2014), discount factor γ (Jiang et al., 2015) and regularizations such as entropy (Ahmed et al., 2018) and weight norms (Cobbe et al., 2018) can also affect generalization.”
• Fast Adaptation to New Environments via Policy-Dynamics Value Functions (2020, background): “…structure, which can lead to faster learning and better generalization to unseen tasks from the same family (Taylor & Stone, 2009; Lazaric, 2012; Ammar et al., 2012; 2014; Parisotto et al., 2015; Borsa et al., 2016; Gupta et al., 2017; Andreas et al., 2017; Oh et al., 2017; Hessel et al., 2019).”
• An exploration strategy for non-stationary opponents (2017, background): “In this cases, a hard-reset like we propose may not be the best approach and we leave that as an open question where ideas from safe-exploration [22,24] or social reward shaping [3] might be worth exploring.”
• AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning (2021, methodology): “This representation is related to Factored MDPs (Kearns and Koller, 1999; Boutilier et al., 2000; Strehl et al., 2007) and Factored POMDPs (Katt et al., 2019), but augmented with change factors that represent a low-dimensional embedding of the changes across domains.”
• Flexible Option Learning (2021, background): “It would be possible to consider different approaches to correct for difference between intra-option policies [Mahmood et al., 2014, Li et al., 2015, Harutyunyan et al., 2016, Jiang and Li, 2016, Jain and Precup, 2018] which we leave for future work.”