Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Lee, Hyunin"'
Black swan events are statistically rare occurrences that carry extremely high risks. A typical view of defining black swan events is heavily assumed to originate from an unpredictable time-varying environments; however, the community lacks a compreh
Externí odkaz:
http://arxiv.org/abs/2407.18422
Real-time inference is a challenge of real-world reinforcement learning due to temporal differences in time-varying environments: the system collects data from the past, updates the decision model in the present, and deploys it in the future. We tack
Externí odkaz:
http://arxiv.org/abs/2405.16053
We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-world applications. In reality, environmental changes occur over wall-
Externí odkaz:
http://arxiv.org/abs/2309.14989
Imitation learning suffers from causal confusion. This phenomenon occurs when learned policies attend to features that do not causally influence the expert actions but are instead spuriously correlated. Causally confused agents produce low open-loop
Externí odkaz:
http://arxiv.org/abs/2307.15980
We study Concave Constrained Markov Decision Processes (Concave CMDPs) where both the objective and constraints are defined as concave functions of the state-action occupancy measure. We propose the Variance-Reduced Primal-Dual Policy Gradient Algori
Externí odkaz:
http://arxiv.org/abs/2205.10715
Entropy regularization is an efficient technique for encouraging exploration and preventing a premature convergence of (vanilla) policy gradient methods in reinforcement learning (RL). However, the theoretical understanding of entropy-regularized RL
Externí odkaz:
http://arxiv.org/abs/2110.10117
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