
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
The NeurIPS 2020 Procgen Competition was designed as a centralized bench...
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The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Although deep reinforcement learning has led to breakthroughs in many di...
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Scaling Laws for Autoregressive Generative Modeling
We identify empirical scaling laws for the crossentropy loss in four do...
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Phasic Policy Gradient
We introduce Phasic Policy Gradient (PPG), a reinforcement learning fram...
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Leveraging Procedural Generation to Benchmark Reinforcement Learning
In this report, we introduce Procgen Benchmark, a suite of 16 procedural...
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Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Monte Carlo Tree Search (MCTS) algorithms perform simulationbased searc...
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SemiSupervised Learning by Label Gradient Alignment
We present label gradient alignment, a novel algorithm for semisupervis...
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Quantifying Generalization in Reinforcement Learning
In this paper, we investigate the problem of overfitting in deep reinfor...
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ModelBased Reinforcement Learning via MetaPolicy Optimization
Modelbased reinforcement learning approaches carry the promise of being...
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Gotta Learn Fast: A New Benchmark for Generalization in RL
In this report, we present a new reinforcement learning (RL) benchmark b...
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Reptile: a Scalable Metalearning Algorithm
This paper considers metalearning problems, where there is a distributio...
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Dexterous multifingered hands are extremely versatile and provide a gen...
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TeacherStudent Curriculum Learning
We propose TeacherStudent Curriculum Learning (TSCL), a framework for a...
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UCB Exploration via QEnsembles
We show how an ensemble of Q^*functions can be leveraged for more effec...
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#Exploration: A Study of CountBased Exploration for Deep Reinforcement Learning
Countbased exploration algorithms are known to perform nearoptimally w...
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RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Deep reinforcement learning (deep RL) has been successful in learning so...
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Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
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Concrete Problems in AI Safety
Rapid progress in machine learning and artificial intelligence (AI) has ...
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
This paper describes InfoGAN, an informationtheoretic extension to the ...
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OpenAI Gym
OpenAI Gym is a toolkit for reinforcement learning research. It includes...
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VIME: Variational Information Maximizing Exploration
Scalable and effective exploration remains a key challenge in reinforcem...
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Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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