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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Accelerating Value Iteration with Anchoring
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In this paper, we present the first accelerated VI for both the Bellman consistency and optimality operators.
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Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement
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D2C requires only a few examples of desired outcomes and works in any environment, regardless of its geometry or the distribution of the desired outcome examples.
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Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
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We show that the proposed method significantly improves the performance of the baseline algorithm in the considered environments.
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