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Greatest papers with code

Implicit Quantile Networks for Distributional Reinforcement Learning

ICML 2018 google/dopamine

In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING

Distributional Reinforcement Learning with Quantile Regression

27 Oct 2017facebookresearch/ReAgent

In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING

QUOTA: The Quantile Option Architecture for Reinforcement Learning

5 Nov 2018ShangtongZhang/DeepRL

In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL).

DECISION MAKING DISTRIBUTIONAL REINFORCEMENT LEARNING

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

NeurIPS 2019 ku2482/fqf-iqn-qrdqn.pytorch

The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING

GAN Q-learning

13 May 2018daggertye/GAN-Q-Learning

Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation.

DISTRIBUTIONAL REINFORCEMENT LEARNING OPENAI GYM Q-LEARNING

Distributional Reinforcement Learning via Moment Matching

24 Jul 2020thanhnguyentang/mmdrl

We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING

Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment

21 Jan 2021thxsxth/POMDP_RLSepsis

Moreover in a safety critical domain it is essential to know what our agent does and does not know, for this we also quantify the model uncertainty associated with each patient state and action, and propose a general framework for uncertainty aware, interpretable treatment policies.

DECISION MAKING UNDER UNCERTAINTY DISTRIBUTIONAL REINFORCEMENT LEARNING SAFE REINFORCEMENT LEARNING

Implicit Distributional Reinforcement Learning

NeurIPS 2020 zhougroup/IDAC

To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution.

DISTRIBUTIONAL REINFORCEMENT LEARNING OPENAI GYM