Advantage Amplification in Slowly Evolving Latent-State Environments
暂无分享,去创建一个
Craig Boutilier | Dale Schuurmans | Ofer Meshi | Martin Mladenov | Jayden Ooi | Dale Schuurmans | Craig Boutilier | Ofer Meshi | Martin Mladenov | Jayden Ooi
[1] Craig Boutilier,et al. Logistic Markov Decision Processes , 2017, IJCAI.
[3] Xiaohui Ye,et al. Horizon: Facebook's Open Source Applied Reinforcement Learning Platform , 2018, ArXiv.
[4] Saeed Shiry Ghidary,et al. Usage-based web recommendations: a reinforcement learning approach , 2007, RecSys '07.
[5] Damien Ernst,et al. On overfitting and asymptotic bias in batch reinforcement learning with partial observability , 2017, J. Artif. Intell. Res..
[6] Vahab S. Mirrokni,et al. Budget Optimization for Online Campaigns with Positive Carryover Effects , 2012, WINE.
[7] Laurent Orseau,et al. Reinforcement Learning with a Corrupted Reward Channel , 2017, IJCAI.
[8] Sergey Levine,et al. Trust Region Policy Optimization , 2015, ICML.
[9] Doina Precup,et al. Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales , 1998 .
[10] Balaraman Ravindran,et al. Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning , 2017, ICLR.
[11] Marc G. Bellemare,et al. Increasing the Action Gap: New Operators for Reinforcement Learning , 2015, AAAI.
[12] Philip S. Thomas,et al. Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees , 2015, IJCAI.
[13] Sridhar Mahadevan,et al. Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..
[14] Guy Shani,et al. An MDP-Based Recommender System , 2002, J. Mach. Learn. Res..
[15] Ajith Ramanathan,et al. Practical Diversified Recommendations on YouTube with Determinantal Point Processes , 2018, CIKM.
[16] Xavier Boyen,et al. Tractable Inference for Complex Stochastic Processes , 1998, UAI.
[17] Michael I. Jordan,et al. Learning Without State-Estimation in Partially Observable Markovian Decision Processes , 1994, ICML.
[18] Milos Hauskrecht,et al. Hierarchical Solution of Markov Decision Processes using Macro-actions , 1998, UAI.
[19] David Veredas,et al. Temporal Aggregation of Univariate and Multivariate Time Series Models: A Survey , 2008 .
[20] Russ Tedrake,et al. Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms , 2008, NIPS.
[21] Dale Schuurmans,et al. Bridging the Gap Between Value and Policy Based Reinforcement Learning , 2017, NIPS.
[22] Liang Zhang,et al. Deep Reinforcement Learning for List-wise Recommendations , 2017, ArXiv.
[23] Avraham Adler,et al. Lambert-W Function , 2015 .
[24] Jung-Woo Ha,et al. Reinforcement Learning based Recommender System using Biclustering Technique , 2018, ArXiv.
[25] Jan Peters,et al. Hierarchical Relative Entropy Policy Search , 2014, AISTATS.
[26] Diane Tang,et al. Focusing on the Long-term: It's Good for Users and Business , 2015, KDD.
[27] Danna Zhou,et al. d. , 1934, Microbial pathogenesis.
[28] Richard S. Sutton,et al. Predictive Representations of State , 2001, NIPS.
[30] Edward J. Sondik,et al. The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..
[31] Amir Massoud Farahmand,et al. Action-Gap Phenomenon in Reinforcement Learning , 2011, NIPS.
[32] Byron Boots,et al. Predictive State Recurrent Neural Networks , 2017, NIPS.
[33] Mohamad Y. Jaber,et al. Learning and forgetting models and their applications , 2013 .
[34] Ronald Parr,et al. Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems , 1998, UAI.
[35] Joelle Pineau,et al. Temporal Regularization in Markov Decision Process , 2018, ArXiv.
[36] L. L. Thurstone,et al. The learning curve equation , 1919 .
[37] Roy Fox,et al. Taming the Noise in Reinforcement Learning via Soft Updates , 2015, UAI.
[38] L. Baird. Reinforcement Learning Through Gradient Descent , 1999 .
[39] Craig Boutilier,et al. SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets , 2019, IJCAI.