Approximate policy iteration with
Approximate Policy Iteration for Markov Control Revisited
... value iteration and remains the most popular choice for solving Markov Decision Problems (MDPs) via reinforcement learning (RL), where the goal is to bypass the transition probabilities of the ...MDP. ...
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Approximate policy iteration: A survey and some new methods
... to approximate policy evaluation, which is an extensively researched and reasonably well understood subject, policy improvement with cost function approximation may exhibit complex behavior that is ...
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Approximate Policy Iteration for Semi-Markov Control Revisited
... value iteration and policy ...policy iteration. In RL, such algorithms are loosely termed as approximate policy iteration (API) ...techniques. Policy ...
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Vision-based reinforcement learning using approximate policy iteration
... Policy iteration consists of two phases: (1) policy evalu- ation, computing the value function Q π (s, a) by solving a set of linear equations, and (2) policy improvement, using Q π (s, a) to ...
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Approximate Policy Iteration for Generalized Semi-Markov Decision Processes: an Improved Algorithm
... We introduce GSMDP with ob- servable time and hybrid state space and present an new algorithm based on Approximate Policy Iteration to generate efficient policies.. This al- gorithm reli[r] ...
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Approximate Policy Iteration (API) with neural networks for the generalized single node energy storage problem
... that approximate policy iteration with neural networks can give good results but the full potential of this approach can only be understood after experimenting with different neural network ...
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Rollout, Approximate Policy Iteration, and Distributed Reinforcement Learning. Chapter 4 Approximate Policy Iteration for Infinite Horizon Problems
... a policy µ, has cost function ˆ J µ = J µ − V in the cost-modified ...by approximate DP methods, such as the ones we have discussed and will discuss further in the next two ...
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Least-Squares Policy Iteration
... least-squares policy-iteration (LSPI) algorithm, which extends the benefits of LSTD to control ...the approximate state-action value function of a fixed policy, thus permitting action ...
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Value-Iteration Based Fitted Policy Iteration: Learning with a Single Trajectory
... fitted policy iteration used in quite a few previous empirical works (see, ...fitted policy iteration when the policies encountered are evaluated using trajectory-based approximation value ...
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Value-iteration based fitted policy iteration: learning with a single trajectory
... fitted policy iteration used in quite a few previous empirical works (see, ...fitted policy iteration when the policies encountered are evaluated using trajectory-based approximation value ...
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Least-Squares Methods for Policy Iteration
... Abstract Approximate reinforcement learning deals with the essential problem of applying reinforcement learning in large and continuous state-action spaces, by us- ing function approximators to represent the ...
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Analysis of Classification-based Policy Iteration Algorithms
... to approximate policy iteration ( API ) called direct policy iteration ( DPI ) and provided its finite-sample performance ...step policy update, 2) considering any policy ...
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Regularized Policy Iteration with Nonparametric Function Spaces
... regularization-based approximate policy iteration algorithms, namely REG- LSPI and REG-BRM, to solve reinforcement learning and planning problems in discounted Markov Decision Processes with large ...
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Finite-Sample Analysis of Least-Squares Policy Iteration
... the policy improvement ...in policy evaluation, an additional term of order γ K is ...in approximate policy iteration and have interesting insights on the concentrability ...the ...
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Performance Bounds for λ Policy Iteration and Application to the Game of Tetris
... λ policy iteration—a family of algorithms parametrized by a pa- rameter λ—that generalizes the standard algorithms value and policy iteration, and has some deep connections with the ...
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Approximate Modified Policy Iteration
... ified policy iteration (MPI), that despite its generality that contains the celebrated policy and value itera- tion methods, has not been thoroughly investigated in the ...three approximate ...
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Approximate Modified Policy Iteration
... ified policy iteration (MPI), that despite its generality that contains the celebrated policy and value itera- tion methods, has not been thoroughly investigated in the ...three approximate MPI ...
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Convergence of Online and Approximate Multiple-Step Lookahead Policy Iteration
... vanilla Policy Iteration algorithms were proposed and ...monotonic policy improvement is not guaranteed unless the update stepsize is sufficiently ...and approximate algorithms that use such a ...
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Approximate Modified Policy Iteration and its Application to the Game of Tetris
... Bertsekas Features: Figures 9(a)-(c) show the performance of CE, λ-PI, DPI, and CBMPI. Here all the approximations in the algorithms are with the Bertsekas features plus constant offset. CE achieves the score 500 after ...
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Supplementary Material: Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations
... produced by the OFVI algorithm. Notice that π ∗ ≡ Φ ∗ in our case, because we have assumed that O contains all primitive actions A. The following lemma develops a pointwise relationship between the V Φ ∗ − V ϕ K and V Φ ...