Reinforcement Learning
"Reinforcement learning is learning what to do – how to map situations to actions – so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them."
– Sutton, Richard S. and Andrew G. Barto. Reinforcement Learning: An Introduction. (1.1). MIT Press, Cambridge, MA, 1998.
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TTOpt: AMaximumVolumeQuantizedTensor Train-basedOptimizationanditsApplicationto ReinforcementLearning
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The vital part of every learning-based algorithm is an optimization procedure, e.g., Stochastic Gradient Descent.
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DistributionallyAdaptiveMetaReinforcement Learning
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The diversity and dynamism of the real world require reinforcement learning (RL) agents that can quickly adapt and learn new behaviors when placed in novel situations.
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VAST: ValueFunctionFactorizationwith VariableAgentSub-Teams
Neural Information Processing Systems
Value function factorization (VFF) is a popular approach to cooperative multiagent reinforcement learning in order to learn local value functions from global rewards.
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