Inverse Reinforcement LearningChelsea Finn

3/5/2017

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Course Reminders: March 22nd: Project group & title due April 17th: Milestone report due & milestone presentations April 26th: Beginning of project presentations

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1. Motivation & Definition 2. Early Approaches 3. Maximum Entropy Inverse RL 4. Scaling inverse RL to deep cost functions

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Inverse RL: Outline

1. Motivation & Definition 2. Early Approaches 3. Maximum Entropy Inverse RL 4. Scaling inverse RL to deep cost functions

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Inverse RL: Outline

Mnih et al. ’15

video from Montessori New Zealand

what is the reward?reinforcement learning agent

reward

In the real world, humans don’t get a score.

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reward function is essential for RL

real-world domains: reward/cost often difficult to specify

• robotic manipulation • autonomous driving • dialog systems • virtual assistants • and more…

Kohl & Stone, ’04 Mnih et al. ’15 Silver et al. ‘16Tesauro ’95

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Behavioral Cloning: Mimic actions of expert

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Motivation

- but no reasoning about outcomes or dynamics

Can we reason about what the expert is trying to achieve?

- the expert might have different degrees of freedom

Inverse Optimal Control / Inverse Reinforcement Learning: infer cost/reward function from expert demonstrations

(Kalman ’64, Ng & Russell ’00)(IOC/IRL)

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Inverse Optimal Control / Inverse Reinforcement Learning: infer cost/reward function from demonstrations

Challenges underdefined problem

difficult to evaluate a learned cost demonstrations may not be precisely optimal

given: - state & action space - roll-outs from π* - dynamics model [sometimes]

goal: - recover reward function - then use reward to get policy

Compare to DAgger: no direct access to π*

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Early IRL Approaches

All: alternate between solving MDP w.r.t. cost and updating cost

Ng & Russell ‘00: expert actions should have higher value than other actions, larger gap is better

Abbeel & Ng ’04: expert policy w.r.t. cost should match feature counts of expert trajectories

Ratliff et al. ’06: max margin formulation between value of expert actions and other actions

How to handle ambiguity? What if expert is not perfect?

1. Motivation & Examples 2. Early Approaches 3. Maximum Entropy Inverse RL 4. Scaling inverse RL to deep cost functions

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Inverse RL: Outline

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Whiteboard

Maximum Entropy Inverse RL(Ziebart et al. ’08)

Notation:

: cost with parameters [linear case ]

: dataset of demonstrations

: transition dynamics

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Maximum Entropy Inverse RL(Ziebart et al. ’08)

1. Motivation & Examples 2. Early Approaches 3. Maximum Entropy IRL 4. Scaling IRL to deep cost functions

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Inverse RL: Outline

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Case Study: MaxEnt Deep IRL MaxEnt IRL with known dynamics (tabular setting), neural net cost

NIPS Deep RL workshop 2015

IROS 2016

Case Study: MaxEnt Deep IRL MaxEnt IRL with known dynamics (tabular setting), neural net cost

15Need to iteratively solve MDP for every weight update

Case Study: MaxEnt Deep IRL MaxEnt IRL with known dynamics (tabular setting), neural net cost

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demonstrations

mean height height variance cell visibility

120km of demonstration data

test-set trajectory prediction:

manually designed cost:

MHD: modified Hausdorff distance

Case Study: MaxEnt Deep IRL MaxEnt IRL with known dynamics (tabular setting), neural net cost

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Strengths - scales to neural net costs - efficient enough for real robots

Limitations - still need to repeatedly solve the MDP - assumes known dynamics

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Whiteboard

What about unknown dynamics?

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Goals: - remove need to solve MDP in the inner loop - be able to handle unknown dynamics - handle continuous state & actions spaces

Case Study: Guided Cost Learning

ICML 2016

Update cost using samples & demos

generate policy samples from π

update π w.r.t. cost

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policy π cost c

guided cost learning algorithm

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policy π0

Update cost using samples & demos

generate policy samples from π

update π w.r.t. cost (partially optimize)

generator

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policy π cost c

discriminator

guided cost learning algorithm

update cost in inner loop of policy optimization

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policy π0

Update cost using samples & demos

generate policy samples from π

generator

Ho et al., ICML ’16, NIPS ‘16

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discriminator

guided cost learning algorithm

update π w.r.t. cost (partially optimize)

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policy π0

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What about unknown dynamics?Adaptive importance sampling

GCL Experiments

dish placement pouring almonds

Real-world Tasks

state includes goal plate pose state includes unsupervised visual features [Finn et al. ’16]

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action: joint torques

Update cost using samples & demos

generate policy samples from q

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ComparisonsRelative Entropy IRL (Boularias et al. ‘11)

Path Integral IRL (Kalakrishnan et al. ‘13)

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Dish placement, demos

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Dish placement, standard cost

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Dish placement, RelEnt IRL

• video of dish baseline method

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Dish placement, GCL policy

• video of dish our method - samples & reoptimizing

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Pouring, demos

• video of pouring demos

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Pouring, RelEnt IRL

• video of pouring baseline method

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Pouring, GCL policy

• video of pouring our method - samples

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Conclusion: We can recover successful policies for new positions.

Is the cost function also useful for new scenarios?

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Dish placement - GCL reopt.

• video of dish our method - samples & reoptimizing

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Pouring - GCL reopt.

• video of pouring our method - reoptimization

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Note: normally the GAN discriminator is discarded

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Case Study: Generative Adversarial Imitation Learning

NIPS 2016

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Case Study: Generative Adversarial Imitation Learning

- demonstrations from TRPO-optimized policy - use TRPO as a policy optimizer - OpenAI gym tasks

Guided Cost Learning & Generative Adversarial Imitation Learning

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Strengths - can handle unknown dynamics - scales to neural net costs - efficient enough for real robots

Limitations - adversarial optimization is hard - can’t scale to raw pixel observations of demos - demonstrations typically collected with kinesthetic

teaching or teleoperation (first person)

Next Time: Back to forward RL (advanced policy gradients)

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IOC is under-definedneed regularization:• encourage slowly changing cost

• cost of demos decreases strictly monotonically in time

Regularization ablationReaching

samples

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Peg Insertion

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ours, demo initours, rand. initours, no lcr reg, demo initours, no lcr reg, rand. initours, no mono reg, demo initours, no mono reg, rand. init