Search Results for author:
Found 12 papers, 3 papers with code
Sample Efficient Actor-Critic with Experience Replay
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.
Distral: Robust Multitask Reinforcement Learning
Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.
Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure.
Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
Relational inductive bias for physical construction in humans and machines
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.
Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.
Structured agents for physical construction
Our results show that agents which use structured representations (e. g., objects and scene graphs) and structured policies (e. g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes.
Deep reinforcement learning with relational inductive biases
We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability.
Hamiltonian Graph Networks with ODE Integrators
We introduce an approach for imposing physically informed inductive biases in learned simulation models.
Object-oriented state editing for HRL
We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems.
Combining Q-Learning and Search with Amortized Value Estimates
In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.
