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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AgentModellingunderPartialObservabilityfor DeepReinforcementLearning
Neural Information Processing Systems
Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution. To eliminate this assumption, we extract representations from thelocalinformation ofthecontrolled agent using encoderdecoderarchitectures.
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I2Q: AFullyDecentralizedQ-LearningAlgorithm
Neural Information Processing Systems
The modeling of ideal transition function inI2Q isfully decentralized and independent from the learned policies of other agents, helping I2Q be free from non-stationarity and learn the optimal policy.
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