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Useful Policy Invariant Shaping from Arbitrary Advice
Reinforcement learning is a powerful learning paradigm in which agents c...
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Maximum Reward Formulation In Reinforcement Learning
Reinforcement learning (RL) algorithms typically deal with maximizing th...
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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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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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Work in Progress: Temporally Extended Auxiliary Tasks
Predictive auxiliary tasks have been shown to improve performance in num...
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Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Reinforcement learning (RL) is a popular paradigm for addressing sequent...
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Multi Type Mean Field Reinforcement Learning
Mean field theory provides an effective way of scaling multiagent reinfo...
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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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Action Guidance with MCTS for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years...
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Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years...
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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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Interactive Learning of Environment Dynamics for Sequential Tasks
In order for robots and other artificial agents to efficiently learn to ...
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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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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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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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Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) has achieved superior performance ...
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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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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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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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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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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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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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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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Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) has achieved superior performance ...
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Learning to Teach Reinforcement Learning Agents
In this article we study the transfer learning model of action advice un...
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Interactive Learning from Policy-Dependent Human Feedback
For agents and robots to become more useful, they must be able to quickl...
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