Robust Reinforcement Learning
A subfield of reinforcement learning that studies methods by which policies maintain acceptable performance when perturbations and uncertainty exist in environment dynamics, observations or reward functions. It is often formulated as worst-case optimization over a set of perturbations.
Timeline (last 90 days)
RobustRLlib integrates 17 representative robust online, offline and safe reinforcement learning algorithms under a unified interface.
Robust reinforcement learning focuses on policies maintaining acceptable performance when environment dynamics, observations or reward functions shift relative to training.
Robust reinforcement learning is typically formalized as worst-case optimization over a perturbation set.
Previously, researchers wanting to compare different robust RL algorithms often had to set up environments and adapt interfaces separately, at very high migration cost.
All Facts (4)
RobustRLlib integrates 17 representative robust online, offline and safe reinforcement learning algorithms under a unified interface.
50%UnverifiedRobust reinforcement learning focuses on policies maintaining acceptable performance when environment dynamics, observations or reward functions shift relative to training.
50%UnverifiedRobust reinforcement learning is typically formalized as worst-case optimization over a perturbation set.
50%UnverifiedPreviously, researchers wanting to compare different robust RL algorithms often had to set up environments and adapt interfaces separately, at very high migration cost.
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