Search Results for author:
Found 14 papers, 5 papers with code
Global Decision-Making via Local Economic Transactions
This paper seeks to establish a mechanism for directing a collection of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems with a central global objective.
Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Object rearrangement is a challenge for embodied agents because solving these tasks requires generalizing across a combinatorially large set of configurations of entities and their locations.
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL).
DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information
However, these cues can be misleading for objects with wide-range variation or adversarial situations, which is a challenging aspect of object-agnostic distance estimation.
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation
Iterative refinement -- start with a random guess, then iteratively improve the guess -- is a useful paradigm for representation learning because it offers a way to break symmetries among equally plausible explanations for the data.
Explore and Control with Adversarial Surprise
Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering.
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
Empirical evidence suggests that such action-value methods are more sample efficient than policy-gradient methods on transfer problems that require only sparse changes to a sequence of previously optimal decisions.
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems.
Entity Abstraction in Visual Model-Based Reinforcement Learning
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before.
Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes
Many approaches to hierarchical reinforcement learning aim to identify sub-goal structure in tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors.
Representational efficiency outweighs action efficiency in human program induction
The importance of hierarchically structured representations for tractable planning has long been acknowledged.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world.
Understanding Visual Concepts with Continuation Learning
We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations.
