Data-Efficient Hierarchical Reinforcement Learning

NeurIPS 2018 1 code implementation

In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.

HIERARCHICAL REINFORCEMENT LEARNING

Data-Efficient Hierarchical Reinforcement Learning

NeurIPS 2018 5 code implementations

In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.

HIERARCHICAL REINFORCEMENT LEARNING

Deep Reinforcement Learning

15 Oct 20185 code implementations

We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.

RLlib: Abstractions for Distributed Reinforcement Learning

ICML 2018 1 code implementation

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation.

Implicit Quantile Networks for Distributional Reinforcement Learning

ICML 2018 10 code implementations

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 REGRESSION

Dopamine: A Research Framework for Deep Reinforcement Learning

14 Dec 20186 code implementations

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

StarCraft II: A New Challenge for Reinforcement Learning

16 Aug 20179 code implementations

Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.

STARCRAFT STARCRAFT II

Deep Reinforcement Learning with Double Q-learning

22 Sep 201540 code implementations

The popular Q-learning algorithm is known to overestimate action values under certain conditions.

ATARI GAMES Q-LEARNING

Asynchronous Methods for Deep Reinforcement Learning

4 Feb 201646 code implementations

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.

ATARI GAMES