
Towards an Understanding of Default Policies in Multitask Policy Optimization
Much of the recent success of deep reinforcement learning has been drive...
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Revisiting Design Choices in ModelBased Offline Reinforcement Learning
Offline reinforcement learning enables agents to leverage large precoll...
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ReplayGuided Adversarial Environment Design
Deep reinforcement learning (RL) agents may successfully generalize to n...
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MiniHack the Planet: A Sandbox for OpenEnded Reinforcement Learning Research
The progress in deep reinforcement learning (RL) is heavily driven by th...
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Graph Kernel Attention Transformers
We introduce a new class of graph neural networks (GNNs), by combining s...
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Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL
Despite a series of recent successes in reinforcement learning (RL), man...
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Same State, Different Task: Continual Reinforcement Learning without Interference
Continual Learning (CL) considers the problem of training an agent seque...
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Augmented World Models Facilitate ZeroShot Dynamics Generalization From a Single Offline Environment
Reinforcement learning from largescale offline datasets provides us wit...
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Unlocking Pixels for Reinforcement Learning via Implicit Attention
There has recently been significant interest in training reinforcement l...
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Deep Reinforcement Learning with Dynamic Optimism
In recent years, deep offpolicy actorcritic algorithms have become a d...
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ESENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning
We introduce ESENAS, a simple neural architecture search (NAS) algorith...
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Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Over the last decade, a single algorithm has changed many facets of our ...
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On Optimism in ModelBased Reinforcement Learning
The principle of optimism in the face of uncertainty is prevalent throug...
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Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometricallydriven optimization ...
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Ready Policy One: World Building Through Active Learning
ModelBased Reinforcement Learning (MBRL) offers a promising direction f...
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OneShot Bayes Opt with Probabilistic Population Based Training
Selecting optimal hyperparameters is a key challenge in machine learning...
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Effective Diversity in PopulationBased Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
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Reinforcement Learning with Chromatic Networks
We present a new algorithm for finding compact neural networks encoding ...
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Wasserstein Reinforcement Learning
We propose behaviordriven optimization via Wasserstein distances (WDs) ...
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Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes
We propose a new class of structured methods for Monte Carlo (MC) sampli...
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Adaptive SampleEfficient Blackbox Optimization via ESactive Subspaces
We present a new algorithm ASEBO for conducting optimization of highdim...
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When random search is not enough: SampleEfficient and NoiseRobust Blackbox Optimization of RL Policies
Interest in derivativefree optimization (DFO) and "evolutionary strateg...
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Jack ParkerHolder
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