Model-Based Reinforcement Learning via Latent-Space Collocation

1 code implementation • 24 Jun 2021 • Oleh Rybkin, Chuning Zhu, Anusha Nagabandi, Kostas Daniilidis, Igor Mordatch, Sergey Levine

The resulting latent collocation method (LatCo) optimizes trajectories of latent states, which improves over previously proposed shooting methods for visual model-based RL on tasks with sparse rewards and long-term goals.

Model-based Reinforcement Learning reinforcement-learning +1

MELD: Meta-Reinforcement Learning from Images via Latent State Models

1 code implementation • 26 Oct 2020 • Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks.

Meta-Learning Meta Reinforcement Learning +3

Deep Dynamics Models for Learning Dexterous Manipulation

2 code implementations • 25 Sep 2019 • Anusha Nagabandi, Kurt Konoglie, Sergey Levine, Vikash Kumar

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills.

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

7 code implementations • NeurIPS 2020 • Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations.

Continuous Control reinforcement-learning +2

Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL

no code implementations • ICLR 2019 • Anusha Nagabandi, Chelsea Finn, Sergey Levine

The goal in this paper is to develop a method for continual online learning from an incoming stream of data, using deep neural network models.

Meta-Learning Model-based Reinforcement Learning

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

2 code implementations • ICLR 2019 • Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.

Continuous Control Meta-Learning +5

Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots

no code implementations • 14 Nov 2017 • Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Ravi Pandya, Gregory Kahn, Sergey Levine, Ronald S. Fearing

We present an approach for controlling a real-world legged millirobot that is based on learned neural network models.

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

8 code implementations • 8 Aug 2017 • Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, Sergey Levine

Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.

Model-based Reinforcement Learning reinforcement-learning +1