Search Results for author: John D. Co-Reyes

Found 8 papers, 4 papers with code

Information is Power: Intrinsic Control via Information Capture

no code implementations NeurIPS 2021 Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine

We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.

RL-DARTS: Differentiable Architecture Search for Reinforcement Learning

no code implementations4 Jun 2021 Yingjie Miao, Xingyou Song, Daiyi Peng, Summer Yue, John D. Co-Reyes, Eugene Brevdo, Aleksandra Faust

Recently, Differentiable Architecture Search (DARTS) has become one of the most popular Neural Architecture Search (NAS) methods successfully applied in supervised learning (SL).

Neural Architecture Search

Evolving Reinforcement Learning Algorithms

3 code implementations ICLR 2021 John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust

Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.

Atari Games Meta-Learning

Ecological Reinforcement Learning

no code implementations22 Jun 2020 John D. Co-Reyes, Suvansh Sanjeev, Glen Berseth, Abhishek Gupta, Sergey Levine

Much of the current work on reinforcement learning studies episodic settings, where the agent is reset between trials to an initial state distribution, often with well-shaped reward functions.

Entity Abstraction in Visual Model-Based Reinforcement Learning

1 code implementation28 Oct 2019 Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine

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.

Latent Variable Models Model-based Reinforcement Learning +4

Guiding Policies with Language via Meta-Learning

2 code implementations ICLR 2019 John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine

However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task.

Imitation Learning Meta-Learning

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

1 code implementation NeurIPS 2017 Justin Fu, John D. Co-Reyes, Sergey Levine

Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes.

Density Estimation

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