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Deep RL

3861 directly classified papers

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Papers

Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning AISTATS 2019
Quantifying Generalization in Reinforcement Learning ICML 2019
Variational Regret Bounds for Reinforcement Learning UAI 2019
Truly Proximal Policy Optimization UAI 2019
Randomized Value Functions via Multiplicative Normalizing Flows UAI 2019
A Bayesian Approach to Robust Reinforcement Learning UAI 2019
Fake It Till You Make It: Learning-Compatible Performance Support UAI 2019
P3O: Policy-on Policy-off Policy Optimization UAI 2019
EMI: Exploration with Mutual Information ICML 2019
Entity Abstraction in Visual Model-Based Reinforcement Learning CORL 2019
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods CORL 2019
Asynchronous Methods for Model-Based Reinforcement Learning CORL 2019
Certified Adversarial Robustness for Deep Reinforcement Learning CORL 2019
Receding Horizon Curiosity CORL 2019
Variational Optimization Based Reinforcement Learning for Infinite Dimensional Stochastic Systems CORL 2019
Dynamic Experience Replay CORL 2019
Worst Cases Policy Gradients CORL 2019
Deep Value Model Predictive Control CORL 2019
Quasi-Newton Trust Region Policy Optimization CORL 2019
MAME : Model-Agnostic Meta-Exploration CORL 2019
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning CORL 2019
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments CORL 2019
Learning Compact Models for Planning with Exogenous Processes CORL 2019
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning CORL 2019
Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models CORL 2019