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Unsupervised Video Object Segmentation for Deep Reinforcement Learning

Machine Learning and Data Analytics SymposiumDoha, Qatar, April 1, 2019

Vikash Goel, Jameson Weng, Pascal Poupart

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Pascal: RBC Borealis AI Research Director

• Research institute funded by RBC

• 5 research centers: – Montreal, Toronto, Waterloo,

Edmonton and Vancouver

• 80 researchers: – Integrated (applied & fundamental) research model

• ML, RL, NLP, computer vision, private AI, knowledge graphs

• We are hiring!

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Pascal: ML Professor at U of Waterloo

• Deep Learning– Automated structure learning, sum-product networks, transfer learning

• Reinforcement learning– Constrained RL, motion-oriented RL, sport analytics

• NLP– Conversational agents, machine translation, automated proofreading

• Theory– Convex relaxations of sum-product networks, characterization of local

optima in mixture models, consistent approximate Bayesian techniques

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Outline

• Background

– Reinforcement learning: data inefficiency

– Solution: self-supervised learning

• MOREL: Motion-Oriented REinforcement Learning

– Unsupervised object & motion recognition

– Faster policy optimization & interpretability

Reference: Goel, Weng, Poupart (2018) Unsupervised Video Object Segmentation for Deep Reinforcement Learning, NeurIPS.

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Reinforcement Learning

Games, robotics, automated trading, autonomous driving, recommender systems, conversational agents, operations research, data center optimization

Agent

Environment

ObservationReward Action

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Data Inefficiency• Most RL successes: simulated environments

• Atari baselines: 40M frames (Schulman et al., 2017)

Atari MuJoCo VizDoom Computer Go

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Image-based RL

imag

e actionsor

values

deep neural network sparse

reward

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Self-supervised learning

• Auxiliary tasks and objectives– Future observation/reward prediction– Past observation prediction (inverse dynamics)– Observation reconstruction (auto-encoder)

Agent

Environment

ObservationReward Action

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Image-based RL• Deep RL:

• Self-supervised RL (auxiliary tasks):

imag

e actionsor

values

deep neural network

dense signal

deep neural networkim

age

next

imag

e

sparse reward

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Prior knowledge• What do you see?

– Humans: moving objects– RL agent: sequence of pixels

seaquest space invaders breakout

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Discovery of relevant features slows down learning

imag

e actionsor

values

deep neural network

sparse reward

Feature extractionPolicy optimization

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Faster LearningCan we learn a policy that automatically segments moving objects and identifies relevant objects?

seaquest space invaders breakout

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Outline• Background

– Reinforcement learning: data inefficiency– Solution: self-supervised learning

• MOREL: Motion-Oriented REinforcement Learning– Unsupervised object & motion recognition– Faster policy optimization & interpretability

Reference: Goel, Weng, Poupart (2018) Unsupervised Video Object Segmentation for Deep Reinforcement Learning, NeurIPS.

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MOREL: Motion-Oriented RL

Unsupervised object segmentation

Only 1% of the frames (random actions)

Faster policy segmentation

Based on object segmentation and motion

Phase 1 Phase 2

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Motion Consistency • Supervised segmentation: labor intensive labeling

• Idea: leverage optical flow (structure from motion)

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SfM-NetVijayanarasimhan, Ricco, Schmid, Sukthankar, Fragkiadaki, SfM-Net: Learning of Structure and Motion from Video, arXiv, 2017.

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SfM-Net predictions (KITTI 2015)

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Simplified 2D SfM-Net

• No skip connection

• Reconstruction loss: DSSIM (structural dissimilarity)

• Flow regularization: L1 loss

• Curriculum: gradually increase !"#$ from 0 to 1

%"#&'()*"+&* = -../0

%"#$ =12

0(2) × 62 7

%899 = %"#&'()*"+&* + !"#$%"#$

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Simplified 2D SfM-Net

Frame 1 Frame 2Masks

(summed)Most salient

mask Optical flow

Brea

kout

Pong

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Unsupervised object segmentationMasks (summed) Most salient mask Optical flow

Spac

e In

vade

rsBe

am R

ider

Seaq

uest

Frame 1 Frame 2Masks

(summed)Most salient

mask Optical flow

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MOREL: Motion-Oriented RLMulti-objective: max $%&'$() and min ,-./0'121,&3$$,$

Comparison with PPOBetter: 25 gamesSimilar: 25 gamesWorse: 9 games

Comparison with A2CBetter: 26 gamesSimilar: 30 gamesWorse: 3 games

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VideosPong

Breakout

Seaquest

Beamrider

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Performance CurvesBreakout

Epis

ode

rew

ards

Frames Frames

Epis

ode

rew

ards

Seaquest

Pong

Beamrider

Pong

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Ablation StudyBreakout

Seaquest Beamrider

FramesFrames

Epis

ode

rew

ards

Epis

ode

rew

ards

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Conclusion• MOREL: Motion-Oriented REinforcement Learning

– Unsupervised object & motion recognition– Faster policy optimization & interpretability

• Future work– 3D environments, physics-based dynamics, object-oriented RL,

model-based RL

Reference: Goel, Weng, Poupart (2018) Unsupervised Video Object Segmentation for Deep Reinforcement Learning, NeurIPS.

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RBC Borealis AI

• Graduating soon?– Join RBC Borealis AI (https://www.borealisai.com)– Email: pascal.poupart@borealisai.com

• Research Institute– Fundamental research (publications)– Applied research (products)

• Topics– RL: automated trading– NLP: news filtering, information extraction, text generation– Computer Vision: satellite-based house valuation– Privacy: differential privacy– Knowledge graphs: recommender systems