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cs.LG2025
Improving Reinforcement Learning Sample-Efficiency using Local Approximation
Mohit Prashant, Arvind Easwaran
In this study, we derive Probably Approximately Correct (PAC) bounds on the asymptotic sample-complexity for RL within the infinite-horizon Markov Decision Process (MDP) setting th…
cs.LG2025
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation
Mohit Prashant, Arvind Easwaran, Suman Das +1
An issue concerning the use of deep reinforcement learning (RL) agents is whether they can be trusted to perform reliably when deployed, as training environments may not reflect re…