Timezone: »
Reinforcement learning (RL) provides a framework for learning goal-directed policies given user-specified rewards. However, since rewards can be sparse and task-specific, we are interested in the problem of learning without rewards, where agents must discover useful behaviors in the absence of domain-specific incentives. Intrinsic motivation is a family of unsupervised RL techniques which develop general objectives for an RL agent to optimize that lead to better exploration or the discovery of skills. In this paper, we propose a new unsupervised RL technique based on an adversarial game which pits two policies against each other to compete over the amount of surprise an RL agent experiences. The policies each take turns controlling the agent. The Explore policy maximizes entropy, putting the agent into surprising or unfamiliar situations. Then, the Control policy takes over and seeks to recover from those situations by minimizing entropy. The game harnesses the power of multi-agent competition to drive the agent to seek out increasingly surprising parts of the environment while learning to gain mastery over them, leading to better exploration and the emergence of complex skills. Theoretically, we show that under certain assumptions, this game pushes the agent to fully explore the latent state space of stochastic, partially-observed environments, whereas prior techniques will not. Empirically, we demonstrate that even with no external rewards, Adversarial Surprise learns more complex behaviors, and explores more effectively than competitive baselines, outperforming intrinsic motivation methods based on active inference, novelty-seeking (Random Network Distillation (RND)), and multi-agent unsupervised RL (Asymmetric Self-Play (ASP)).
Author Information
Arnaud Fickinger (UC Berkeley)
Natasha Jaques (Google Brain, UC Berkeley)
Samyak Parajuli (UC Berkeley)
Michael Chang (UC Berkeley)
Nicholas Rhinehart (University of California Berkeley)
Glen Berseth (UC Berkeley)
Stuart Russell (UC Berkeley)
Sergey Levine (University of Washington)
More from the Same Authors
-
2020 : Contributed Talk: Multi-Principal Assistance Games »
Arnaud Fickinger · Stuart Russell -
2021 : Reinforcement Learning as One Big Sequence Modeling Problem »
Michael Janner · Qiyang Li · Sergey Levine -
2021 : Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments »
Nicholas Rhinehart · Jenny Wang · Glen Berseth · John Co-Reyes · Danijar Hafner · Chelsea Finn · Sergey Levine -
2021 : Continual Meta Policy Search for Sequential Multi-Task Learning »
Glen Berseth · Zhiwei Zhang -
2021 : ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors »
Charles Sun · Jedrzej Orbik · Coline Devin · Abhishek Gupta · Glen Berseth · Sergey Levine -
2022 : Effective Offline RL Needs Going Beyond Pessimism: Representations and Distributional Shift »
Xinyang Geng · Kevin Li · Abhishek Gupta · Aviral Kumar · Sergey Levine -
2022 : DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning »
Quan Vuong · Aviral Kumar · Sergey Levine · Yevgen Chebotar -
2022 : Distributionally Adaptive Meta Reinforcement Learning »
Anurag Ajay · Dibya Ghosh · Sergey Levine · Pulkit Agrawal · Abhishek Gupta -
2022 : You Only Live Once: Single-Life Reinforcement Learning via Learned Reward Shaping »
Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn -
2022 : Multimodal Masked Autoencoders Learn Transferable Representations »
Xinyang Geng · Hao Liu · Lisa Lee · Dale Schuurmans · Sergey Levine · Pieter Abbeel -
2022 : Multimodal Masked Autoencoders Learn Transferable Representations »
Xinyang Geng · Hao Liu · Lisa Lee · Dale Schuurmans · Sergey Levine · Pieter Abbeel -
2022 Poster: AnyMorph: Learning Transferable Polices By Inferring Agent Morphology »
Brandon Trabucco · mariano phielipp · Glen Berseth -
2022 Spotlight: AnyMorph: Learning Transferable Polices By Inferring Agent Morphology »
Brandon Trabucco · mariano phielipp · Glen Berseth -
2021 Poster: Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment »
Michael Chang · Sid Kaushik · Sergey Levine · Thomas Griffiths -
2021 Oral: Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment »
Michael Chang · Sid Kaushik · Sergey Levine · Thomas Griffiths -
2021 Poster: Emergent Social Learning via Multi-agent Reinforcement Learning »
Kamal Ndousse · Douglas Eck · Sergey Levine · Natasha Jaques -
2021 Poster: PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning »
Angelos Filos · Clare Lyle · Yarin Gal · Sergey Levine · Natasha Jaques · Gregory Farquhar -
2021 Spotlight: Emergent Social Learning via Multi-agent Reinforcement Learning »
Kamal Ndousse · Douglas Eck · Sergey Levine · Natasha Jaques -
2021 Oral: PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning »
Angelos Filos · Clare Lyle · Yarin Gal · Sergey Levine · Natasha Jaques · Gregory Farquhar -
2020 Poster: Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions »
Michael Chang · Sid Kaushik · S. Matthew Weinberg · Thomas Griffiths · Sergey Levine -
2019 Workshop: Generative Modeling and Model-Based Reasoning for Robotics and AI »
Aravind Rajeswaran · Emanuel Todorov · Igor Mordatch · William Agnew · Amy Zhang · Joelle Pineau · Michael Chang · Dumitru Erhan · Sergey Levine · Kimberly Stachenfeld · Marvin Zhang -
2019 Poster: Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning »
Natasha Jaques · Angeliki Lazaridou · Edward Hughes · Caglar Gulcehre · Pedro Ortega · DJ Strouse · Joel Z Leibo · Nando de Freitas -
2019 Oral: Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning »
Natasha Jaques · Angeliki Lazaridou · Edward Hughes · Caglar Gulcehre · Pedro Ortega · DJ Strouse · Joel Z Leibo · Nando de Freitas -
2018 Poster: Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms »
Yi Wu · Siddharth Srivastava · Nicholas Hay · Simon Du · Stuart Russell -
2018 Oral: Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms »
Yi Wu · Siddharth Srivastava · Nicholas Hay · Simon Du · Stuart Russell -
2017 Poster: Sequence Tutor: Conservative fine-tuning of sequence generation models with KL-control »
Natasha Jaques · Shixiang Gu · Dzmitry Bahdanau · Jose Miguel Hernandez-Lobato · Richard E Turner · Douglas Eck -
2017 Talk: Sequence Tutor: Conservative fine-tuning of sequence generation models with KL-control »
Natasha Jaques · Shixiang Gu · Dzmitry Bahdanau · Jose Miguel Hernandez-Lobato · Richard E Turner · Douglas Eck