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.
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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Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
Neural Information Processing Systems
Our approach combines three key ideas: (1) value-demand augmentation, (2) action-space approximate dynamic programming, and (3) time-space rounding.
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Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees Dohyeong Kim
Neural Information Processing Systems
However, the nonlinearity of risk measures makes it challenging to achieve convergence and optimality.
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