Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study

By Shivaram Kalyanakrishnan, Yaxin Liu, Peter Stone. 2006

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics

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Citations in the corpus

The StarCraft Multi-Agent Challenge (2019, background): “This domain was later extended to the Half Field Offense task (Kalyanakrishnan et al., 2006; Hausknecht et al., 2016), which increases the difﬁculty of the problem by requiring the agents to not only keep the ball within bounds but also to score a goal.”
Source Task Creation for Curriculum Learning (2016, background): “[7], we focus on learning behaviors for the player with the ball.”
Cooperation and communication in multiagent deep reinforcement learning (2016, background): “Kalyanakrishnan has explored the communication of values between agents (Kalyanakrishnan et al., 2007).”
Deep Reinforcement Learning in Parameterized Action Space (2015, methodology): “Challenges in multi-agent learning in the RoboCup domain have been examined by prior work (Kalyanakrishnan et al., 2007) and solutions may translate into the deep reinforcement learning settings as well.”
Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork (2016, background): “[12] almost a decade ago, but the authors did not release code for their framework.”
Online Planning for Large Markov Decision Processes with Hierarchical Decomposition (2015, background): “They have also extended their work to a more general task named half-field offense [Kalyanakrishnan et al. 2007].”
Batch reinforcement learning in a complex domain (2007, background): “The latter approach has been shown in the past to speed up learning in Half Field Offense [3], a task that extends Keepaway to a goal shooting”