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.
Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration
Neural Information Processing Systems
Reinforcement learning (RL) algorithms are typically based on optimizing a Markov Decision Process (MDP) using the optimal Bellman equation.
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The Value of Reward Lookahead in Reinforcement Learning
Neural Information Processing Systems
In reinforcement learning (RL), agents sequentially interact with changing environments while aiming to maximize the obtained rewards.
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CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
Neural Information Processing Systems
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.
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