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.
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
Neural Information Processing Systems
We further discuss the sample complexity for a desired approximation accuracy.
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Diffusion-Reward Adversarial Imitation Learning
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Despite its encouraging results, GAIL training is often brittle and unstable.
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Effective Exploration Based on the Structural Information Principles
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The SI2E analyzes value differences in the agent's policy between state-action
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Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms
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Based on our analysis, we propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
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