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An Efficient Solution to s-Rectangular Robust Markov Decision Processes
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abstract
We present an efficient robust value iteration for \texttt{s}-rectangular robust Markov Decision Processes (MDPs) with a time complexity comparable to standard (non-robust) MDPs which is significantly faster than any existing method. We do so by deriving the optimal robust Bellman operator in concrete forms using our $L_p$ water filling lemma. We unveil the exact form of the optimal policies, which turn out to be novel threshold policies with the probability of playing an action proportional to its advantage.
Forward citations
Cited by 3 Pith papers
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Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis
RHI is claimed to find an epsilon-optimal robust policy under the average-reward criterion with about SAH^2/epsilon^2 samples under the communicating assumption, with a parameter-free variant that avoids knowing H.
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Distributionally Robust Deep Q-Learning
Sinkhorn-ball robust Bellman equation for continuous-state MDPs is implemented as a Robust DQN that learns policies robust to transition-model misspecification.
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Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
A pessimism-based framework for zero-shot transfer RL builds conservative proxies from robust MDPs, yielding lower-bound performance guarantees and distributed algorithms that mitigate negative transfer.
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