Chelsea Finn

Meta Reinforcement Learning

Why are humans so good at RL?

People have prior experience.

Can we learn a representation under which RL is fast?

People have an existing representation of the world.

Key idea: Explicitly optimize for such a representation

“Learn how to reinforcement learn”

Outline

Meta-RL Problem Formulation & Examples

Method Classes: Recurrent Models, Gradient-Based Models

Challenges & Latest Developments

The Meta-Learning Problem

Inputs: Outputs:Supervised Learning:

Meta Supervised Learning:Inputs: Outputs:

Data:

Data:

Why is this view useful? Reduces the problem to the design & optimization of f.

{

Finn & Levine. Meta-learning and Universality: Deep Representation… ICLR 2018

Example: Few-Shot Classification

diagram adapted from Ravi & Larochelle ‘17

Given 1 example of 5 classes: Classify new examples

meta-training

training data test set

training classes

… …

Meta-RL Example: Maze Navigation

diagram adapted from Duan et al. ‘17

Given a small amount of experience Learn to solve the task

By learning how to learn many other tasks:

…

The Meta Reinforcement Learning Problem

Inputs: Outputs:Reinforcement Learning:

Meta Reinforcement Learning:Inputs: Outputs:

Data:

Data:

Finn. Learning to Learn with Gradients. PhD Thesis 2018

{

k rollouts from dataset of datasets

collected for each task

Design & optimization of f *and* collecting appropriate data(learning to explore)

diagram adapted from Duan et al. ‘17

Given a small amount of experience Learn to solve the task

By learning how to learn many other tasks:

…

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meta-training tasks

Meta-RL Example: Maze Navigation

The Meta Reinforcement Learning ProblemMeta Reinforcement Learning:

Inputs: Outputs:{

k rollouts from

Online Variant

Episodic Variant

{1…k timesteps from

Inputs: Outputs:

Outline

Meta-RL Problem Formulation & Examples

Method Classes: Recurrent Models, Gradient-Based Models

Challenges & Latest Developments

Design of f ?Recurrent network

(LSTM, NTM, Conv)Santoro et al. ’16, Duan et al. ’17, Wang et al. ’17, Mishra et al. ’17, …

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…

Hidden state maintained across episodes within a task!

Difference compared to using a recurrent policy?

Case Study: Simple Neural Attentive Meta-LearnerMishra et al, ICLR ‘18

Simple Neural Attentive Meta-LearnerMishra et al, ICLR ‘18

1D convolutions

attention

interleave 1D convolutions and attention

1D convolutions

attention

Simple Neural Attentive Meta-LearnerMishra et al, ICLR ‘18

Experiment: Learning to visually navigate a maze - train on 1000 small mazes - test on held-out small mazes and large mazes

Simple Neural Attentive Meta-LearnerMishra et al, ICLR ‘18

Experiment: Learning to visually navigate a maze - train on 1000 small mazes - test on held-out small mazes and large mazes

Digression: Connection to Contextual Policies

Contextual policy with experience as context.

What about goal-conditioned policies / value functions?- rewards are a strict generalization of goals - meta-RL objective is to adapt new tasks vs. generalize to new goals

(k-shot vs. 0-shot)

Design of f ?Recurrent network

(LSTM, NTM, Conv)Santoro et al. ’16, Duan et al. ’17, Wang et al. ’17, Munkhdalai & Yu ’17, Mishra et al. ’17, …

+ general & expressive + a variety of design choices in architecture

-- complex model for complex task of learning -- impractical data requirements

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…

Key idea: Train over many tasks, to learn parameter vector θ that transfers

Fine-tuningtraining data for new task

pretrained parameters

Our method

[test-time]

Finn, Abbeel, Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017

optimal parameter vector for task i

parameter vector being meta-learned

Model-Agnostic Meta-Learning

Finn, Abbeel, Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017Can we learn a representation under which RL is fast?

Fast Adaptation in Reinforcement Learning

Finn, Abbeel, Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017

Fast Adaptation in Reinforcement Learning

Finn, Abbeel, Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017

Design of f ?Recurrent network MAML

network implements the “learned learning procedure”

Does it converge?

What does it converge to?

What to do if not good enough?

Does it converge? - Yes (it’s gradient descent…)

What does it converge to? - A local optimum (it’s gradient descent…)

What to do if not good enough? - Keep taking gradient steps (it’s gradient descent..)

- Who knows…

- Nothing

- Sort of?

“consistency”

Does this structure come at a cost?

Outline

Meta-RL Problem Formulation & Examples

Method Classes: Recurrent Models, Gradient-Based Models

Challenges & Latest Developments

Wishlist in a Meta-RL algorithm

Consistent

Expressive

Structured Exploration

Efficient & off-policy

RL2, SNAIL MAML-PG

~ ~

*Initial work in this direction: Gupta et al. NIPS ’18, Stadie et al. NIPS ‘18

*

Model-based RL

Collect data Fit model

‘’

Plan using the model

Goal: learn to adapt model quickly to new environments (online system ID)

Model-based RL already achieves zero-shot adaptation to new rewards.

Anusha Nagabandi Ignasi Clavera Simin Liu

Goal: learn to adapt model quickly to new environments

motor malfunctiongradual terrain change

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Goal: learn to adapt model quickly to new environments

motor malfunctiongradual terrain change

time

online adaptation = few-shot learning

The task distribution is more-or-less free!

tasks are temporal slices of experience

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Meta-learning using either:Dynamics RNN (ReBAL)

MAML (GrBAL)

Dynamic Environments with Online Adaptation

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Roach RobotMeta-train on variable terrains Meta-test with slope, missing leg, payload, calibration errors

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Meta-train on variable terrains Meta-test with slope, missing leg, payload, calibration errors

Roach Robot

GrBAL Model-Based RL(no adaptation)

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Meta-train on variable terrains Meta-test with slope, missing leg, payload, calibration errors

Roach Robot

GrBAL Model-Based RL(no adaptation)

Nagabandi*, Clavera*, Liu, Fearing, Abbeel, Levine, Finn. Learning to Adapt in Dynamic Real-World Environments through Meta-RL

Wishlist in a Meta-RL algorithm

Consistent

Expressive

Structured Exploration

Efficient & off-policy

RL2, SNAIL MAML-PG

~ ~

ReBAL, GrBAL

~

One more important challenge:

Still lots of room for exploration & improvement!

where do the tasks come from?

So far: manual defined distribution of tasks.(and corresponding rewards)

Unsupervised Meta-RL

Gupta, Eysenbach, Finn, Levine. Unsupervised Meta-Learning for Reinforcement Learning.

environmentUnsupervised Meta-RL

Meta-learned environment-specific

RL algorithm

reward-maximizing policy

reward function

Unsupervised Task Acquisition Meta-RL

Fast Adaptation

General Recipe

Abhishek Gupta Ben Eysenbach Sergey Levine

Random Task Proposals

■ Use randomly initialize discriminators for reward functions

■ Important: Random functions over state space, not random policies

1 2 3 … nRandom policy – exponentialRandom reward – polynomial

D ! randomly initialized network

Diversity-Driven Proposals

Policy(Agent)

Discriminator(D)

Skill (z)

Environment

Action State

Predict Skill

■ Policy ! visit states which are discriminable

■ Discriminator ! predict skill from state

Task Reward for UML:

Eysenbach, Gupta, Ibarz, Levine. Diversity is All You Need.

Examples of Acquired Tasks

Eysenbach, Gupta, Ibarz, Levine. Diversity is All You Need.

Cheetah Ant

Does it work?

Gupta, Eysenbach, Finn, Levine. Unsupervised Meta-Learning for Reinforcement Learning.

2D Navigation Cheetah Ant

Meta-test performance with rewards

Takeaway: Relatively simple mechanisms for proposing tasks work surprisingly well.

Key ReferencesRecurrent Models: Yan Duan et al. ’17 (RL^2), Jane Wang et al. CogSci ’17 (Learning to Reinforcement Learn), Nikhil Mishra et al. ICLR ’18 (SNAIL) Model-Agnostic Meta-Learning: Finn et al. ICML ’17

Further ReadingValue-based: Sung et al. arXiv ’17 (Meta-critic networks) Exploration: Gupta et al. NIPS ’18, Stadie et al. NIPS ’18 Unsupervised: Gupta et al. arXiv ’18 (Unsupervised Meta-Learning for Reinforcement Learning) Model-based: Nagabandi*, Clavera* et al. arXiv ’18 Evolutionary Strategies: Houthooft et al. NIPS ’18 Cognitive Science: Wang et al. Nature Neuroscience ’18 (PFC as a Meta-RL System)

Takeaway

- a meta-RL algorithm that is both consistent and expressive - meta-RL algorithms that learn to explore in a structured way - constructing task distributions for meta-RL in an automated way - meta-RL algorithms that can incorporate off-policy data effectively

Don’t reinforcement learn from scratch.Meta-RL provides an approach to do this.

Open Problems

Sergey Levine Pieter Abbeel Anusha Nagabandi Ignasi Clavera Simin Liu Abhishek Gupta Ben EysenbachCollaborators

Ron Fearing