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Found 7 papers, 0 papers with code
Neural Abstract Reasoner
Abstract reasoning and logic inference are difficult problems for neural networks, yet essential to their applicability in highly structured domains.
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer
The agent improves its policy in simulations generated by the DNC model and rolls out the policy to the live environment, collecting experiences in new portions or tasks of the environment for further learning.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Programmatically Structured Representations with Perceptor Gradients
We present the perceptor gradients algorithm -- a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions.
From explanation to synthesis: Compositional program induction for learning from demonstration
This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators.
Using Program Induction to Interpret Transition System Dynamics
Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions.
Grounding Symbols in Multi-Modal Instructions
As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground symbols in the physical world.
Explaining Transition Systems through Program Induction
Explaining and reasoning about processes which underlie observed black-box phenomena enables the discovery of causal mechanisms, derivation of suitable abstract representations and the formulation of more robust predictions.
