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Provably Efficient Exploration in Policy Optimization
While policy-based reinforcement learning (RL) achieves tremendous succe...
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Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
We consider the exploration-exploitation dilemma in finite-horizon reinf...
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Provably Efficient Reinforcement Learning with Linear Function Approximation
Modern Reinforcement Learning (RL) is commonly applied to practical prob...
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Off-Policy Interval Estimation with Lipschitz Value Iteration
Off-policy evaluation provides an essential tool for evaluating the effe...
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Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret
We study risk-sensitive reinforcement learning in episodic Markov decisi...
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Exploration-Exploitation in Constrained MDPs
In many sequential decision-making problems, the goal is to optimize a u...
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Better safe than sorry: Risky function exploitation through safe optimization
Exploration-exploitation of functions, that is learning and optimizing a...
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Provably Efficient Safe Exploration via Primal-Dual Policy Optimization
We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation in which an agent aims to maximize the expected total reward subject to a safety constraint on the expected total value of a criterion function (e.g., utility). We focus on an episodic setting with the function approximation where the reward and criterion functions and the Markov transition kernels all have a linear structure but do not impose any additional assumptions on the sampling model. Designing SRL algorithms with provable computational and statistical efficiency is particularly challenging under this setting because of the need to incorporate both the safety constraint and the function approximation into the fundamental exploitation/exploration tradeoff. To this end, we present an Optimistic Primal-Dual Proximal Policy OPtimization (OPDOP) algorithm where the value function is estimated by combining the least-squares policy evaluation and an additional bonus term for safe exploration. We prove that the proposed algorithm achieves an O(d^1.5H^3.5√(T)) regret and an O(d^1.5H^3.5√(T)) constraint violation, where d is the dimension of the feature mapping, H is the horizon of each episode, and T is the total number of steps. We establish these bounds under the following two settings: (i) Both the reward and criterion functions can change adversarially but are revealed entirely after each episode. (ii) The reward/criterion functions are fixed but the feedback after each episode is bandit. Our bounds depend on the capacity of the state space only through the dimension of the feature mapping and thus our results hold even when the number of states goes to infinity. To the best of our knowledge, we provide the first provably efficient policy optimization algorithm for CMDPs with safe exploration.
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