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**1. Algorithms for Inverse Reinforcement Learning 2**

1. Algorithms for Inverse Reinforcement Learning 2. Apprenticeship learning via Inverse Reinforcement Learning

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**Algorithms for Inverse Reinforcement Learning Andrew Ng and Stuart Russell**

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Motivation Given: (1) measurements of an agent's behavior over time, in a variety of circumstances, (2) if needed, measurements of the sensory inputs to that agent; (3) if available, a model of the environment. Determine: the reward function being optimized.

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**Why? Reason #1: Computational models for animal and human learning.**

“In examining animal and human behavior we must consider the reward function as an unknown to be ascertained through empirical investigation.” Particularly true of multiattribute reward functions (e.g. Bee foraging: amount of nectar vs. flight time vs. risk from wind/predators)

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**Why? Reason #2: Agent construction.**

“An agent designer [...] may only have a very rough idea of the reward function whose optimization would generate 'desirable' behavior.” e.g. “Driving well” Apprenticeship learning: Recovering expert's underlying reward function more “parsimonious” than learning expect's policy?

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**Possible applications in multi-agent systems**

In multi-agent adversarial games, learning opponents’ reward functions that guild their actions to devise strategies against them. example In mechanism design, learning each agent’s reward function from histories to manipulate its actions. and more?

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**Inverse Reinforcement Learning (1) – MDP Recap**

MDP is represented as a tuple (S, A, {Psa}, ,R) Note: R is bounded by Rmax Value function for policy : Q-function:

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**Inverse Reinforcement Learning (1) – MDP Recap**

Bellman Equation: Bellman Optimality:

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**Inverse Reinforcement Learning (2) Finite State Space**

Reward function solution set (a1 is optimal action)

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**Inverse Reinforcement Learning (2) Finite State Space**

There are many solutions of R that satisfy the inequality (e.g. R = 0), which one might be the best solution? 1. Make deviation from as costly as possible: 2. Make reward function as simple as possible

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**Inverse Reinforcement Learning (2) Finite State Space**

Va1 Va2 maximized Linear Programming Formulation: a1, a2, …, an R?

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**Inverse Reinforcement Learning (3) Large State Space**

Linear approximation of reward function (in driving example, basis functions can be collision, stay on right lane,…etc) Let be value function of policy , when reward R = For R to make optimal

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**Inverse Reinforcement Learning (3) Large State Spaces**

In an infinite or large number of state space, it is usually not possible to check all constraints: Choose a finite subset S0 from all states Linear Programming formulation, find αi that: x>=0, p(x)=x; otherwise p(x)=2x

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**Inverse Reinforcement Learning (4) IRL from Sample Trajectories**

If is only accessible through a set of sampled trajectories (e.g. driving demo in 2nd paper) Assume we start from a dummy state s0,(whose next state distribution is according to D). In the case that reward trajectory state sequence (s0, s1, s2….):

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**Inverse Reinforcement Learning (4) IRL from Sample Trajectories**

Assume we have some set of policies Linear Programming formulation The above optimization gives a new reward R, we then compute based on R, and add it to the set of policies reiterate

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**Discrete Gridworld Experiment**

5x5 grid world Agent starts in bottom-left square. Reward of 1 in the upper-right square. Actions = N,W,S,E (30% chance of random)

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**Discrete Gridworld Results**

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**Mountain Car Experiment #1**

Car starts in valley, goal is at the top of hill Reward is -1 per “step” until goal is reached State = car's x-position & velocity (continuous!) Function approx. class: all linear combinations of 26 evenly spaced Gaussian-shaped basis functions

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**Mountain Car Experiment #2**

Goal is in bottom of valley Car starts... not sure. Top of hill? Reward is 1 in the goal area, 0 elsewhere γ = 0.99 State = car's x-position & velocity (continuous!) Function approx. class: all linear combinations of 26 evenly spaced Gaussian-shaped basis functions

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Mountain Car Results #1 #2

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**Continuous Gridworld Experiment**

State space is now [0,1] x [0,1] continuous grid Actions: 0.2 movement in any direction + noise in x and y coordinates of [-0.1,0.1] Reward 1 in region [0.8,1] x [0.8,1], 0 elsewhere γ = 0.9 Function approx. class: all linear combinations of a 15x15 array of 2-D Gaussian-shaped basis functions m=5000 trajectories of 30 steps each per policy

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**Continuous Gridworld Results**

3%-10% error when comparing fitted reward's optimal policy with the true optimal policy However, no significant difference in quality of policy (measured using true reward function)

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**Apprenticeship Learning via Inverse Reinforcement Learning**

Pieter Abbeel & Andrew Y. Ng

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**Algorithm For t = 1,2,… Inverse RL step:**

Estimate expert’s reward function R(s)= wT(s) such that under R(s) the expert performs better than all previously found policies {i}. RL step: Compute optimal policy t for the estimated reward w. Convey intuition about what the algorithm is doing. Find reward that makes teacher look much better; and then hypotesize that reward and find the policy. The intuition behind the algorithm, is that the set of policies generated gets closer (in some sense) to the expert at every iteration. RL black box. Inverse RL: explain the details now. Courtesy of Pieter Abbeel

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**Algorithm: IRL step Maximize , w:||w||2≤ 1 **

s.t. Vw(E) Vw(i) + i=1,…,t-1 = margin of expert’s performance over the performance of previously found policies. Vw() = E [t t R(st)|] = E [t t wT(st)|] = wT E [t t (st)|] = wT () () = E [t t (st)|] are the “feature expectations” \mu(\pi) can be computed for any policy \pi based on the transition dynamics ‘’maybe mention – due to norm constraint -> QP, same as max margin for svm’s ; details poster’’ Performance guarantees … next slide Courtesy of Pieter Abbeel

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**Feature Expectation Closeness and Performance**

If we can find a policy such that ||(E) - ()||2 , then for any underlying reward R*(s) =w*T(s), we have that |Vw*(E) - Vw*()| = |w*T (E) - w*T ()| ||w*||2 ||(E) - ()||2 . Let’s now look at how we could get performance guarantees, although the reward function is unknown. The key is that, if we can find a policy that is \epsilon close to the expert in feature expectations, then no matter what the underlying, unknown, reward function R* is, that policy will have performance \epsilon close to the expert’s performance as measure w.r.t. the unknown reward function. This is easily seen as follows. Going back to our algorithm, at every iteration it finds a policy closer to the expert in terms of feature expectations as follows. I will now illustrate this intuition graphically. In the inverse RL step it finds the direction in feature space for which the expert is mostly separated from the previously found policies. In the RL step, it finds a policy which is optimal w.r.t. this estimated reward, which brings it closer to the expert, again, in feature expectation space. Motivation for algorithm. --- get closer in \mu; Courtesy of Pieter Abbeel

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**IRL step as Support Vector Machine**

maximum margin hyperplane seperating two sets of points () (E) |w*T (E) - w*T ()| = |Vw*(E) - Vw*()| = maximal difference between expert policy’s value function and 2nd to the optimal policy’s value function

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**2 (E) (2) w(3) (1) w(2) w(1) (0) 1 Uw() = wT()**

Courtesy of Pieter Abbeel

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Gridworld Experiment 128 x 128 grid world divided into 64 regions, each of size 16 x 16 (“macrocells”). A small number of macrocells have positive rewards. For each macrocell, there is one feature Φi(s) indicating whether that state s is in macrocell i Algorithm was also run on the subset of features Φi(s) that correspond to non-zero rewards.

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**Gridworld Results Distance to expert vs. # Iterations**

Performance vs. # Trajectories

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**Car Driving Experiment**

No explict reward function at all! Expert demonstrates proper policy via 2 min. of driving time on simulator (1200 data points). 5 different “driver types” tried. Features: which lane the car is in, distance to closest car in current lane. Algorithm run for 30 iterations, policy hand- picked. Movie Time! (Expert left, IRL right)

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Demo-1 Nice

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Demo-2 Right Lane Nasty

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Car Driving Results

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© D. Weld and D. Fox 1 Reinforcement Learning CSE 473.

© D. Weld and D. Fox 1 Reinforcement Learning CSE 473.

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