Policy gradient
Policy Gradient in Continuous Time
... the gradient because they are subject to variance explosion when the discretization time-step decreases to ...a policy gradient estimate that converges almost surely to the true gradient when ...
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Chinese Grammatical Error Diagnosis Based on Policy Gradient LSTM Model
... where N denotes a negative label, ie there is no wrong label, P denotes a positive label, ie there is a wrong label. We can see that the proportion of positive and negative sample labels in a not very long sentence is ...
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Learning of Soccer Player Agents Using a Policy Gradient Method: Pass Selection
... A policy gradient method is applied as a learning method to solve this problem because it can easily express the various heuristics of pass selection in a policy ...
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Policy-Gradient Algorithms for Partially Observable Markov Decision Processes
... the policy space, but not with the number of states in the ...for policy evaluation and is compared to them under various optimisation methods by Strens and Moore ...Monte-Carlo ...
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Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient
... However, most existing RL techniques cannot handle the large discrete action space problem in IRS as the time com- plexity of making a decision is linear to the size of the ac- tion space. Specifically, all Deep ...
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Diverse Exploration via Conjugate Policies for Policy Gradient Methods
... Policy gradient (PG) (Peters and Schaal 2008; Schulman et al. 2015; Sutton et al. 1999; Wu et al. 2017) methods in reinforcement learning (RL) (Sutton and Barto 1998) have shown the ability to train large ...
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Multi Task Semantic Dependency Parsing with Policy Gradient for Learning Easy First Strategies
... applying policy gradient train- ing to several constituency parsers, including the RNNG transition-based parser (Dyer et ...with policy gradient did not always perform better than the models ...
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Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing
... objective’s gradient; however since in the parsing tasks we consider, the gold tree has constant and minimal cost, augmenting with the gold is equivalent to jointly optimizing the standard likelihood and risk ...
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Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient
... agent’s policy can easily get stuck in a poor local optima ...learned policy may be only locally optimal to other agents’ current ...Deterministic Policy Gradient (M3DDPG) with the fol- lowing ...
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Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade
... In this paper, two policy gradient algorithms are compared with one value-function based method and two variants of the popular Roth-Erev technique [11]. A power exchange auction market model is used to ...
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Bayesian Policy Gradient and Actor-Critic Algorithms
... based) policy gradient estimation ...the gradient of the expected return with respect to the policy parameters, which is of the form of an integral, as Gaussian processes ...the ...
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Policy Gradient Methods: Variance Reduction and Stochastic Convergence
... of policy gradient al- gorithms, in particular, when augmenting the estimate with a baseline, a common method for reducing estimation variance, and when using actor-critic ...a policy gradient ...
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Temporal difference Learning with Sampling Baseline for Image Captioning
... by policy gradient method in reinforcement learning domain attributable to its unique capability of directly opti- mizing the discrete and non-differentiable evaluation ...
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Towards Coherent and Cohesive Long form Text Generation
... Notice that this reward resembles the ranking loss we use to train our discriminators, except that our baseline is the mean score (instead of the weighted mean) over negative pairs. The ra- tionale for this difference is ...
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A kernel based true online Sarsa(λ) for continuous space control problems
... Abstract. Reinforcement learning is an efficient learning method for the control problem by interacting with the environment to get an optimal policy. However, it also faces challenges such as low convergence ...
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Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
... trajectory-based policy gradient algorithm is presented in Section 3 and its convergence analysis is provided in Appendix A (Appendix ...the gradient estimates of the CVaR parameter, the ...
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Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
... Deterministic Policy Gradient (DDPG) method used by [114] in stock trading demonstrated that how the model is able to handle large setting concerning stability, improving data using, and equilibrating risk ...
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Research and Application of the Novel Deep Plugging Method in the Oilfield
... In the actual displacing process, the strength loss of plugging agent is affected by perforation, ground flow, well head, well spacing and so on, and the perfora- tion loss attains 40%. Considering various factors, ...
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The weighted gradient: A color image gradient applied to morphological segmentation
... If a band presents all possible values in same quan- tities (i.e., its histogram has an uniform distribution), its gradient should receive the maximum weight. To do this, we consider that there is an ”ideal image” ...
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The gradient of a graph
... the gradient function, because an appropriate formula is not ...the gradient is initially large and positive, diminishing to zero for x=0 and staying zero thereafter, again being able to make a rough ...the ...
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