Markov decision processes: a tool for sequential decision making under uncertainty
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New framework enhances probabilistic safety for autonomous agents
Researchers have developed a new formal framework for probabilistic safety shields in Markov Decision Processes (MDPs). This framework addresses the complexities of ensuring safety when a certain probability of undesira…
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Q-MMR framework offers novel approach to off-policy evaluation
Researchers have introduced Q-MMR, a new theoretical framework for off-policy evaluation in Markov Decision Processes (MDPs). This method learns weights for data points to approximate expected returns under a target pol…
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Actor-Critic RL algorithms achieve optimal sample complexity for MDPs
Two new arXiv papers explore advancements in actor-critic reinforcement learning algorithms. The first paper, though later withdrawn, proposed an optimal sample complexity of O(ε−2) for single-timescale actor-critic met…
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New method learns uncertain MDPs with tighter parameter estimates
Researchers have developed a new method for learning models of Markov decision processes (MDPs) that accounts for dependencies between transition probabilities. This approach uses parametric MDPs (pMDPs) to represent tr…