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Found 5 papers, 0 papers with code
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima
Existing algorithms designed for such a problem were applicable to restricted situations and do not come with a full guarantee of convergence.
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process
Despite the challenge of the nonconcave objective subject to nonconcave constraints, the proposed approach is shown to converge to the global optimum with a complexity of $\tilde{\mathcal O}(1/\epsilon)$ in terms of the optimality gap and the constraint violation, which improves the complexity of the existing primal-dual approach by a factor of $\mathcal O(1/\epsilon)$ \citep{ding2020natural, paternain2019constrained}.
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method
Although ETD has been shown to converge asymptotically to a desirable value function, it is well-known that ETD often encounters a large variance so that its sample complexity can increase exponentially fast with the number of iterations.
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
Generative adversarial imitation learning (GAIL) is a popular inverse reinforcement learning approach for jointly optimizing policy and reward from expert trajectories.
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack
This paper investigates the attack model where an adversary attacks with a certain probability at each round, and its attack value can be arbitrary and unbounded if it attacks.
