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Learning Mesh-Based Simulation with Graph Networks
Mesh-based simulations are central to modeling complex physical systems ...
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Discovering Symbolic Models from Deep Learning with Inductive Biases
We develop a general approach to distill symbolic representations of a l...
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Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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Combining Q-Learning and Search with Amortized Value Estimates
We introduce "Search with Amortized Value Estimates" (SAVE), an approach...
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Object-oriented state editing for HRL
We introduce agents that use object-oriented reasoning to consider alter...
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Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive bias...
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Structured agents for physical construction
Physical construction -- the ability to compose objects, subject to phys...
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Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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Graph networks as learnable physics engines for inference and control
Understanding and interacting with everyday physical scenes requires ric...
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