Found 1059 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

Our experiments on multiple continuous control tasks demonstrate the effectiveness of our approach compared to state-of-the-art imitation and inverse reinforcement learning methods. Expand abstract.
3 days ago
7/10 relevant
arXiv

MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design

To fill this gap, we formulate the controller synchronization problem as a Markov decision process (MDP) and apply reinforcement learning techniques combined with deep neural networks (DNNs) to train a smart, scalable, and fine-grained controller synchronization policy, called the Multi-Armed Cooperative Synchronization... Expand abstract.
4 days ago
10/10 relevant
arXiv

How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?

However, RoboticReinforcement Learning currently lacks clearly defined benchmark tasks, which makes it difficult for researchers to reproduce and compare against prior work. ``Reacher'' tasks, which are fundamental to robotic manipulation, are commonly used as benchmarks, but the lack of a formal specification elides... Expand abstract.
4 days ago
10/10 relevant
arXiv

Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator

Taking inspiration from such works and from the recent applications of Reinforcement Learning to visual tracking, we propose two novel trackers, A3CT, which exploits demonstrations of a state-of-the-art tracker to learn an effective tracking policy, and A3CTD, that takes advantage of the same expert tracker to correct... Expand abstract.
5 days ago
10/10 relevant
arXiv

ModelicaGym: Applying Reinforcement Learning to Modelica Models

This paper presents ModelicaGym toolbox that was developed to employ Reinforcement Learning (RL) for solving optimization and control tasks in Modelica models. Expand abstract.
5 days ago
10/10 relevant
arXiv

Segregation Dynamics with Reinforcement Learning and Agent Based Modeling

In this paper, we combine Reinforcement Learning (RL) with Agent Based Models (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of incentives. Expand abstract.
5 days ago
10/10 relevant
arXiv

Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games

We use multiagent reinforcement learning (MARL) to learn opponent models through self-play, which captures the full strategy interaction and reasoning between agents. Expand abstract.
5 days ago
10/10 relevant
arXiv

Multi-Robot Deep Reinforcement Learning with Macro-Actions

Multi-agent reinforcement learning methods have difficulty learning decentralized policies because the environment appearing to be non-stationary due to other agents also learning at the same time. Expand abstract.
5 days ago
10/10 relevant
arXiv

A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming

Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. Expand abstract.
5 days ago
4/10 relevant
arXiv

Multi-agent reinforcement learning for market microstructure statistical inference

In order to address this, we present here a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. Expand abstract.
6 days ago
10/10 relevant
arXiv