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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Rewiring Neurons in Non-Stationary Environments
Neural Information Processing Systems
We are inspired to harness this key process in continual reinforcement learning, prioritizing adaptation to non-stationary environments.
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58a799d16fb0c1f2014e98f4ba972b25-Paper-Conference.pdf
Neural Information Processing Systems
RL that utilize function approximation to generalize observational data to unknown states/actions. The goal of this paper is to study the sample complexity of policy-based RL, which is arguably the simplest setting for RL with function approximation (Kearns et al., 1999; Kakade, 2003).
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