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06/10/2022
Large-Scale Retrieval for Reinforcement Learning
Effective decision making involves flexibly relating past experiences an...
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04/19/2022
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
We consider the offline constrained reinforcement learning (RL) problem,...
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02/17/2022
Retrieval-Augmented Reinforcement Learning
Most deep reinforcement learning (RL) algorithms distill experience into...
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04/13/2021
Muesli: Combining Improvements in Policy Optimization
We propose a novel policy update that combines regularized policy optimi...
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11/18/2020
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring ...
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11/08/2020
On the role of planning in model-based deep reinforcement learning
Model-based planning is often thought to be necessary for deep, careful ...
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10/03/2020
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Intelligent robots need to achieve abstract objectives using concrete, s...
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09/11/2020
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Recent work in deep reinforcement learning (RL) has produced algorithms ...
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02/19/2020
Value-driven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (...
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11/19/2019
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
Constructing agents with planning capabilities has long been one of the ...
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10/01/2019
Augmenting learning using symmetry in a biologically-inspired domain
Invariances to translation, rotation and other spatial transformations a...
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01/11/2019
An investigation of model-free planning
The field of reinforcement learning (RL) is facing increasingly challeng...
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11/15/2018
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Learning policies on data synthesized by models can in principle quench ...
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02/13/2018
Learning to Search with MCTSnets
Planning problems are among the most important and well-studied problems...
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12/05/2017
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of ar...
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07/19/2017
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
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12/28/2016
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models ...
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02/24/2016
Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function ...
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12/15/2015
Increasing the Action Gap: New Operators for Reinforcement Learning
This paper introduces new optimality-preserving operators on Q-functions...
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02/09/2014
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian...
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05/14/2012
