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Supervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine

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Page 1: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Supervised Learning of Behaviors

CS 285: Deep Reinforcement Learning, Decision Making, and Control

Sergey Levine

Page 2: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Class Notes

1. Homework 1 is out this evening

2. Remember to start forming final project groups• Final project assignment document is now out!

• Proposal due Sep 25

Page 3: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Today’s Lecture

1. Definition of sequential decision problems

2. Imitation learning: supervised learning for decision makinga. Does direct imitation work?

b. How can we make it work more often?

3. A little bit of theory

4. Case studies of recent work in (deep) imitation learning

• Goals:• Understand definitions & notation

• Understand basic imitation learning algorithms

• Understand tools for theoretical analysis

Page 4: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

1. run away

2. ignore

3. pet

Terminology & notation

Page 5: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

1. run away

2. ignore

3. pet

Terminology & notation

Page 6: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Aside: notation

Richard Bellman Lev Pontryagin

управление

Page 7: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Images: Bojarski et al. ‘16, NVIDIA

trainingdata

supervisedlearning

Imitation Learning

behavioral cloning

Page 8: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Does it work? No!

Page 9: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Does it work? Yes!

Video: Bojarski et al. ‘16, NVIDIA

Page 10: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Why did that work?

Bojarski et al. ‘16, NVIDIA

Page 11: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Can we make it work more often?

cost

stability (more on this later)

Page 12: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Can we make it work more often?

Page 13: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Can we make it work more often?

DAgger: Dataset Aggregation

Ross et al. ‘11

Page 14: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

DAgger Example

Ross et al. ‘11

Page 15: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

What’s the problem?

Ross et al. ‘11

Page 16: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Can we make it work without more data?

• DAgger addresses the problem of distributional “drift”

• What if our model is so good that it doesn’t drift?

• Need to mimic expert behavior very accurately

• But don’t overfit!

Page 17: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Why might we fail to fit the expert?

1. Non-Markovian behavior

2. Multimodal behavior

behavior depends only on current observation

If we see the same thing twice, we do the same thing twice, regardless of what happened before

Often very unnatural for human demonstrators

behavior depends on all past observations

Page 18: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

How can we use the whole history?

variable number of frames, too many weights

Page 19: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

How can we use the whole history?

RNN state

RNN state

RNN state

shared weights

Typically, LSTM cells work better here

Page 20: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Aside: why might this work poorly?

“causal confusion” see: de Haan et al., “Causal Confusion in Imitation Learning”

Question 1: Does including history exacerbate causal confusion?

Question 2: Can DAgger mitigate causal confusion?

Page 21: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Why might we fail to fit the expert?

1. Non-Markovian behavior

2. Multimodal behavior1. Output mixture of

Gaussians

2. Latent variable models

3. Autoregressive discretization

Page 22: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Why might we fail to fit the expert?

1. Output mixture of Gaussians

2. Latent variable models

3. Autoregressive discretization

Page 23: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Why might we fail to fit the expert?

1. Output mixture of Gaussians

2. Latent variable models

3. Autoregressive discretization

Look up some of these:• Conditional variational autoencoder• Normalizing flow/realNVP• Stein variational gradient descent

Page 24: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Why might we fail to fit the expert?

1. Output mixture of Gaussians

2. Latent variable models

3. Autoregressive discretization

(discretized) distribution over dimension 1 only

discrete sampling

discrete sampling

dim 1 value

dim 2 value

Page 25: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Imitation learning: recap

• Often (but not always) insufficient by itself• Distribution mismatch problem

• Sometimes works well• Hacks (e.g. left/right images)

• Samples from a stable trajectory distribution

• Add more on-policy data, e.g. using Dagger

• Better models that fit more accurately

trainingdata

supervisedlearning

Page 26: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Break

Page 27: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Case study 1: trail following as classification

Page 28: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and
Page 29: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Imitation learning: what’s the problem?

• Humans need to provide data, which is typically finite• Deep learning works best when data is plentiful

• Humans are not good at providing some kinds of actions

• Humans can learn autonomously; can our machines do the same?• Unlimited data from own experience

• Continuous self-improvement

Page 30: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

1. run away

2. ignore

3. pet

Terminology & notation

Page 31: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Aside: notation

Richard Bellman Lev Pontryagin

Page 32: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

A cost function for imitation?

trainingdata

supervisedlearning

Ross et al. ‘11

Page 33: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Some analysis How bad is it?

Page 34: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

More general analysis

For more analysis, see Ross et al. “A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning”

Page 35: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Another imitation idea

Page 36: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Goal-conditioned behavioral cloning

Page 37: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and
Page 38: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

1. Collect data 2. Train goal conditioned policy

Page 39: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

3. Reach goals

Page 40: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

1. run away

2. ignore

3. pet

Terminology & notation

Page 41: Supervised Learning of Behaviorsrail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdfSupervised Learning of Behaviors CS 285: Deep Reinforcement Learning, Decision Making, and

Cost/reward functions in theory and practice


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