Search Results for author: Glen Berseth

Found 23 papers, 6 papers with code

CoMPS: Continual Meta Policy Search

no code implementations8 Dec 2021 Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine

Beyond simply transferring past experience to new tasks, our goal is to devise continual reinforcement learning algorithms that learn to learn, using their experience on previous tasks to learn new tasks more quickly.

Continual Learning Continuous Control +3

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.

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation

no code implementations28 Jul 2021 Charles Sun, Jędrzej Orbik, Coline Devin, Brian Yang, Abhishek Gupta, Glen Berseth, Sergey Levine

Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions.

Continual Learning

Explore and Control with Adversarial Surprise

1 code implementation ICML Workshop URL 2021 Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine

Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering.

Unsupervised Reinforcement Learning

Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments

no code implementations ICML Workshop URL 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.

DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies

no code implementations23 Apr 2021 Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine

Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity.

Accelerating Online Reinforcement Learning via Model-Based Meta-Learning

no code implementations ICLR Workshop Learning_to_Learn 2021 John D Co-Reyes, Sarah Feng, Glen Berseth, Jie Qui, Sergey Levine

Current reinforcement learning algorithms struggle to quickly adapt to new situations without large amounts of experience and usually without large amounts of optimization over that experience.


Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks

no code implementations1 Jan 2021 Glen Berseth, Florian Golemo, Christopher Pal

It would be desirable for a reinforcement learning (RL) based agent to learn behaviour by merely watching a demonstration.

One-Shot 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.

Morphology-Agnostic Visual Robotic Control

no code implementations31 Dec 2019 Brian Yang, Dinesh Jayaraman, Glen Berseth, Alexei Efros, Sergey Levine

Existing approaches for visuomotor robotic control typically require characterizing the robot in advance by calibrating the camera or performing system identification.

Inter-Level Cooperation in Hierarchical Reinforcement Learning

1 code implementation5 Dec 2019 Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco, Samyak Parajuli, Sergey Levine, Alexandre M. Bayen

This allows us to draw on connections between communication and cooperation in multi-agent RL, and demonstrate the benefits of increased cooperation between sub-policies on the training performance of the overall policy.

Hierarchical Reinforcement Learning

Contextual Imagined Goals for Self-Supervised Robotic Learning

1 code implementation23 Oct 2019 Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, Sergey Levine

When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand.

Towards Learning to Imitate from a Single Video Demonstration

no code implementations22 Jan 2019 Glen Berseth, Florian Golemo, Christopher Pal

We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration.

Imitation Learning One-Shot Learning

Visual Imitation Learning with Recurrent Siamese Networks

no code implementations27 Sep 2018 Glen Berseth, Christopher J. Pal

In this paper we propose an approach using only visual information to learn a distance metric between agent behaviour and a given video demonstration.

Imitation Learning

Terrain RL Simulator

1 code implementation17 Apr 2018 Glen Berseth, Xue Bin Peng, Michiel Van de Panne

We provide $89$ challenging simulation environments that range in difficulty.

Feedback Control For Cassie With Deep Reinforcement Learning

3 code implementations15 Mar 2018 Zhaoming Xie, Glen Berseth, Patrick Clary, Jonathan Hurst, Michiel Van de Panne

By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL.


Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

no code implementations ICLR 2018 Glen Berseth, Cheng Xie, Paul Cernek, Michiel Van de Panne

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment.

Continuous Control Transfer Learning

Model-Based Action Exploration for Learning Dynamic Motion Skills

no code implementations11 Jan 2018 Glen Berseth, Michiel Van de Panne

Deep reinforcement learning has achieved great strides in solving challenging motion control tasks.

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