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A policy gradient method for confounded pomdps
In this paper, we propose a policy gradient method for confounded partially observable
Markov decision processes (POMDPs) with continuous state and observation spaces in the …
Markov decision processes (POMDPs) with continuous state and observation spaces in the …
Provably efficient offline reinforcement learning in regular decision processes
R Cipollone, A Jonsson, A Ronca… - Advances in Neural …, 2023 - proceedings.neurips.cc
This paper deals with offline (or batch) Reinforcement Learning (RL) in episodic Regular
Decision Processes (RDPs). RDPs are the subclass of Non-Markov Decision Processes …
Decision Processes (RDPs). RDPs are the subclass of Non-Markov Decision Processes …
Provably efficient ucb-type algorithms for learning predictive state representations
The general sequential decision-making problem, which includes Markov decision
processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at …
processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at …
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a
dataset consisting only of suboptimal trials. One way that this can happen is by" stitching" …
dataset consisting only of suboptimal trials. One way that this can happen is by" stitching" …
Learn to teach: Improve sample efficiency in teacher-student learning for sim-to-real transfer
Simulation-to-reality (sim-to-real) transfer is a fundamental problem for robot learning.
Domain Randomization, which adds randomization during training, is a powerful technique …
Domain Randomization, which adds randomization during training, is a powerful technique …