
Counterfactual Credit Assignment in ModelFree Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring ...
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On the role of planning in modelbased deep reinforcement learning
Modelbased planning is often thought to be necessary for deep, careful ...
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Representation Learning via Invariant Causal Mechanisms
Selfsupervised learning has emerged as a strategy to reduce the relianc...
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Beyond TabulaRasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Intelligent robots need to achieve abstract objectives using concrete, s...
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Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
Recent work in deep reinforcement learning (RL) has produced algorithms ...
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Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
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DivideandConquer Monte Carlo Tree Search For GoalDirected Planning
Standard planners for sequential decision making (including Monte Carlo ...
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Valuedriven Hindsight Modelling
Value estimation is a critical component of the reinforcement learning (...
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Causally Correct Partial Models for Reinforcement Learning
In reinforcement learning, we can learn a model of future observations a...
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Combining QLearning and Search with Amortized Value Estimates
We introduce "Search with Amortized Value Estimates" (SAVE), an approach...
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Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions
A plethora of problems in AI, engineering and the sciences are naturally...
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Credit Assignment Techniques in Stochastic Computation Graphs
Stochastic computation graphs (SCGs) provide a formalism to represent st...
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Woulda, Coulda, Shoulda: CounterfactuallyGuided Policy Search
Learning policies on data synthesized by models can in principle quench ...
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Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in modelbased reinforcement learning (RL) is to synthes...
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Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Calcium imaging permits optical measurement of neural activity. Since in...
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ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
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Learning modelbased planning from scratch
Conventional wisdom holds that modelbased planning is a powerful approa...
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Black box variational inference for state space models
Latent variable timeseries models are among the most heavily used tools...
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Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LLLVM)
We introduce the Locally Linear Latent Variable Model (LLLVM), a probab...
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Lars Buesing
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