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Latest papers without code

Exact Reduction of Huge Action Spaces in General Reinforcement Learning

18 Dec 2020

In this work we show how action-binarization in the non-MDP case can significantly improve Extreme State Aggregation (ESA) bounds.

BINARIZATION GENERAL REINFORCEMENT LEARNING PROTEIN FOLDING STARCRAFT

Causal variables from reinforcement learning using generalized Bellman equations

29 Oct 2020

To our knowledge, this is the first attempt to apply causal analysis in a reinforcement learning setting without strict restrictions on the number of states.

GENERAL REINFORCEMENT LEARNING

Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors

15 Jul 2020

The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i. e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter).

CURRICULUM LEARNING DEVELOPMENTAL LEARNING

Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents

20 Apr 2020

A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards.

DECISION MAKING GENERAL REINFORCEMENT LEARNING

Student/Teacher Advising through Reward Augmentation

7 Feb 2020

Transfer learning is an important new subfield of multiagent reinforcement learning that aims to help an agent learn about a problem by using knowledge that it has gained solving another problem, or by using knowledge that is communicated to it by an agent who already knows the problem.

GENERAL REINFORCEMENT LEARNING TRANSFER LEARNING

Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning

28 Oct 2019

We develop a general reinforcement learning framework for mean field control (MFC) problems.

GENERAL REINFORCEMENT LEARNING Q-LEARNING

Goal-Driven Sequential Data Abstraction

ICCV 2019

In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction.

GENERAL REINFORCEMENT LEARNING

Compositional Transfer in Hierarchical Reinforcement Learning

26 Jun 2019

The successful application of general reinforcement learning algorithms to real-world robotics applications is often limited by their high data requirements.

GENERAL REINFORCEMENT LEARNING HIERARCHICAL REINFORCEMENT LEARNING TRANSFER LEARNING

Variational Regret Bounds for Reinforcement Learning

14 May 2019

This is the first variational regret bound for the general reinforcement learning setting.

GENERAL REINFORCEMENT LEARNING

Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder

22 Mar 2019

Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis.

GENERAL REINFORCEMENT LEARNING