Lenient learners in cooperative multiagent systems

By Liviu Panait, Keith Sullivan, Sean Luke. 2006

78 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Reinforcement Learning

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Citations in the corpus

Decentralized Likelihood Quantile Networks for Improving Performance in Deep Multi-Agent Reinforcement Learning (2018, methodology): “Lenient Learning (Panait et al., 2006) schedules the decrease of leniency applied to individual state-action pairs using decaying temperatures, where leniency is the probability of ignoring a negative Q value update.”
Deep Reinforcement Learning for Swarm Systems (2018, methodology): “Following this work, Palmer et al. [2017] add leniency [Panait et al., 2006] to the hysteretic approach to prevent “relative overgener-alization” of agents.”
Lenient Multi-Agent Deep Reinforcement Learning (2017, methodology): “In this work we apply leniency [22] to MA-DRL.”
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems (2012, methodology): “Notably, we won’t study lenient learners because parameters tuning is too difﬁcult and results of this method are empirically found to be equivalent to FMQ (Panait et al., 2006).”
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective (2008, methodology): “In previous work, we have demonstrated the efficacy of algorithms based on this notion: specifically, the Lenient Multiagent Q-learning algorithm (Panait et al., 2006) and the iCCEA algorithm (Panait and Luke, 2006).”
Lenient Learning in Independent-Learner Stochastic Cooperative Games (2016, methodology): “This is a heavy revision of the original LMRL algorithm, which was originally meant only for repeated games (Panait et al., 2006b).”
Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments (2018, methodology): “As for the non-stationary coexisting agents, we incorporate the leniency from lenient Q-learning [ Palmer et al. , 2018; Panait et al. , 2006] into the reward approximator to provide an optimistic estimation of the expected reward under each state-action pair r ( s, a ) .”
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning (2018, methodology): “Lenient Deep Q-Network (LDQN) [28] incorporates lenient learning [29] with DQN by encoding the high-dimensional state space into lower dimensions where temperature values are feasible to be stored and updated.”
Multi-agent deep reinforcement learning: a survey (2021, methodology): “This phenomenon arises since each agent’s policy performs relatively well when paired with arbitrary actions from other agents (Panait et al. 2006; Wei and Luke 2016; Wiegand 2004).”