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07/10/2023
Can You Improve My Code? Optimizing Programs with Local Search
This paper introduces a local search method for improving an existing pr...
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02/13/2023
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Tomorrow's robots will need to distinguish useful information from noise...
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11/14/2022
NeurIPS 2022 Competition: Driving SMARTS
Driving SMARTS is a regular competition designed to tackle problems caus...
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10/13/2022
Augmenting Flight Training with AI to Efficiently Train Pilots
We propose an AI-based pilot trainer to help students learn how to fly a...
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04/25/2022
Reinforcement Teaching
We propose Reinforcement Teaching: a framework for meta-learning in whic...
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04/14/2022
Methodical Advice Collection and Reuse in Deep Reinforcement Learning
Reinforcement learning (RL) has shown great success in solving many chal...
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03/16/2022
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Learning to collaborate is critical in Multi-Agent Reinforcement Learnin...
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11/15/2021
Learning Representations for Pixel-based Control: What Matters and Why?
Learning representations for pixel-based control has garnered significan...
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04/11/2021
The Atari Data Scraper
Reinforcement learning has made great strides in recent years due to the...
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03/07/2021
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning
Some reinforcement learning methods suffer from high sample complexity c...
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02/19/2021
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Accuracy and generalization of dynamics models is key to the success of ...
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02/02/2021
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
Reinforcement learning (RL) is a popular machine learning paradigm for g...
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01/18/2021
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging
Cooperative multi-agent reinforcement learning (MARL) has achieved signi...
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11/02/2020
Useful Policy Invariant Shaping from Arbitrary Advice
Reinforcement learning is a powerful learning paradigm in which agents c...
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10/08/2020
Maximum Reward Formulation In Reinforcement Learning
Reinforcement learning (RL) algorithms typically deal with maximizing th...
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09/29/2020
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
Experience replay (ER) improves the data efficiency of off-policy reinfo...
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07/03/2020
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
A long-term goal of reinforcement learning agents is to be able to perfo...
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04/01/2020
Work in Progress: Temporally Extended Auxiliary Tasks
Predictive auxiliary tasks have been shown to improve performance in num...
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03/10/2020
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Reinforcement learning (RL) is a popular paradigm for addressing sequent...
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02/06/2020
Multi Type Mean Field Reinforcement Learning
Mean field theory provides an effective way of scaling multiagent reinfo...
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07/26/2019
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
How to best explore in domains with sparse, delayed, and deceptive rewar...
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07/25/2019
Action Guidance with MCTS for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years...
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07/24/2019
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years...
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07/22/2019
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
In this paper we explore how actor-critic methods in deep reinforcement ...
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07/19/2019
Interactive Learning of Environment Dynamics for Sequential Tasks
In order for robots and other artificial agents to efficiently learn to ...
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04/20/2019
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
The Pommerman Team Environment is a recently proposed benchmark which in...
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04/10/2019
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Safe reinforcement learning has many variants and it is still an open re...
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04/03/2019
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) algorithms are known to be data ineffi...
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12/21/2018
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) has achieved superior performance ...
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11/30/2018
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Deep reinforcement learning (DRL) has achieved great successes in recent...
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11/17/2018
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach
Reinforcement learning (RL) techniques, while often powerful, can suffer...
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10/12/2018
Is multiagent deep reinforcement learning the answer or the question? A brief survey
Deep reinforcement learning (DRL) has achieved outstanding results in re...
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05/15/2018
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects
Reinforcement Learning (RL) can be extremely effective in solving comple...
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05/11/2018
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration
Reinforcement learning has enjoyed multiple successes in recent years. H...
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05/10/2018
Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control
Reinforcement learning (RL) has had many successes in both "deep" and "s...
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09/14/2017
Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning
Reinforcement learning (RL), while often powerful, can suffer from slow ...
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09/12/2017
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) has achieved superior performance ...
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07/28/2017
Learning to Teach Reinforcement Learning Agents
In this article we study the transfer learning model of action advice un...
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01/21/2017