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This paper provides a refined theoretical analysis of actor-critic methods with entropy regularization, showing that an exact critic acts as a strong variance reducer and enables sample complexity comparable to deterministic policy gradient, and that with a sufficiently accurate learned critic the benefits are preserved.
This paper introduces Good Policy Identification (GPI) in reinforcement learning, aiming to find a policy meeting a reward threshold rather than the optimal one, and proposes the BEE-GPI algorithm with near-optimal sample complexity guarantees.
This paper studies risk-sensitive reinforcement learning in finite discounted MDPs with a generative model, focusing on the sample complexity of learning optimal value functions and policies under the optimized certainty equivalent (OCE) risk measure. It provides exact conditions for PAC-learnability, analyzes a model-based approach, and establishes tight lower bounds, including an improved dependence on the risk parameter for CVaR.
This paper provides the first non-asymptotic sample complexity bounds for learning exponential families of polynomials with score matching, showing polynomial dependence on model dimension.