Inverse reinforcement learning from summary data

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1 Inverse reinforcement learning from summary data Antti Kangasrääsiö, Samuel Kaski Aalto University, Finland ECML PKDD 2018 journal track Published in Machine Learning (2018), 107: September 12, 2018 Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

2 Modelling human decision-making: Motivation Our overarching goal is to have accurate white-box models of human decision-making Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

3 Modelling human decision-making: Motivation Our overarching goal is to have accurate white-box models of human decision-making Applications of high-fidelity user models Replicating demonstrated behavior (imitation learning) Optimizing user interfaces (human-computer interaction) Estimating cognitive state/goals of humans (chatbots) Understanding human cognition (cognitive science) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

4 Modelling human decision-making: Problem How to infer the parameters of sequential decision-making models when the available observation data is limited? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

5 Modelling human decision-making: Problem How to infer the parameters of sequential decision-making models when the available observation data is limited? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

6 Modelling human decision-making: Problem How to infer the parameters of sequential decision-making models when the available observation data is limited? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

7 Modelling human decision-making: Problem How to infer the parameters of sequential decision-making models when the available observation data is limited? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

Modelling human decision-making: Problem How to infer the parameters of sequential decision-making models when the available observation data is limited?

8 Modelling human decision-making: Problem How to infer the parameters of sequential decision-making models when the available observation data is limited? Main contribution: We demonstrate that posterior inference is possible for realistic models of decision-making, even with very limited observations of human behavior Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

9 Reinforcement learning models We use the RL framework for modelling sequential decision-making The main assumption is that human decisions can be approximated by an optimal policy trained for a certain decision problem (eg. MDP, POMDP) Humans make rational decisions within the limitations they have Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

10 Inverse reinforcement learning (IRL) Inverse reinforcement learning: Given a set of observations, which MDP has a matching optimal policy? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

11 Inverse reinforcement learning (IRL) Inverse reinforcement learning: Given a set of observations, which MDP has a matching optimal policy? Traditional IRL problem Given an MDP with reward-function R(s; θ), θ unknown a set of state-action trajectories Ξ = {ξ 1,..., ξ N } demonstrating optimal behavior, where ξ i = (s i 0, ai 1,..., ai T i 1, si T i ) a prior P(θ) Determine a point estimate ˆθ or the posterior P(θ Ξ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

12 Existing solutions Traditional IRL has been gradient descent on the likelihood L(θ Ξ) = N i=1 T i 1 P(s0) i πθ (si t, at)p(s i t+1 s i t, i at) i t=0 Tractable when all states and actions are observed what about when this is not the case? 1 Activity forecasting, Kitani et al EM for IRL with hidden data, Bogert et al Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

13 Existing solutions Traditional IRL has been gradient descent on the likelihood L(θ Ξ) = N i=1 T i 1 P(s0) i πθ (si t, at)p(s i t+1 s i t, i at) i t=0 Tractable when all states and actions are observed what about when this is not the case? Previous work: If state observations are corrupted with i.i.d. noise 1 or part of them are missing 2, EM-approach can be used to estimate the true states, after which standard IRL methods apply 1 Activity forecasting, Kitani et al EM for IRL with hidden data, Bogert et al Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

14 Existing solutions Traditional IRL has been gradient descent on the likelihood L(θ Ξ) = N i=1 T i 1 P(s0) i πθ (si t, at)p(s i t+1 s i t, i at) i t=0 Tractable when all states and actions are observed what about when this is not the case? Previous work: If state observations are corrupted with i.i.d. noise 1 or part of them are missing 2, EM-approach can be used to estimate the true states, after which standard IRL methods apply However, this approach is not feasible in the more realistic cases, with complex non-i.i.d. noise or most of the states and actions missing 1 Activity forecasting, Kitani et al EM for IRL with hidden data, Bogert et al Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

15 IRL from summary data (IRL-SD) We ask whether IRL is possible in realistic cases, where the true trajectories ξ i are filtered through a generic summarizing function σ, yielding summaries ξ iσ σ(ξ i ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

IRL from summary data (IRL-SD) We ask whether IRL is possible in realistic cases, where the true trajectories ξ i are filtered through a generic summarizing function σ, yielding summaries ξ iσ σ(ξ i

16 IRL from summary data (IRL-SD) We ask whether IRL is possible in realistic cases, where the true trajectories ξ i are filtered through a generic summarizing function σ, yielding summaries ξ iσ σ(ξ i ) Example: Alice walks to work every day along her preferred secret route. Could we infer Alice s scenery preferences given only the durations of the commutes and the location of her work and home? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

17 IRL from summary data (IRL-SD) We ask whether IRL is possible in realistic cases, where the true trajectories ξ i are filtered through a generic summarizing function σ, yielding summaries ξ iσ σ(ξ i ) Example: Alice walks to work every day along her preferred secret route. Could we infer Alice s scenery preferences given only the durations of the commutes and the location of her work and home? IRL from summary data (IRL-SD) problem Given an MDP with unknown parameters θ a set of summaries Ξ σ = {ξ 1σ,..., ξ Nσ } from optimal behavior the summary function σ a prior P(θ) Determine a point estimate ˆθ or the posterior P(θ Ξ σ ). Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

18 Exact solution The likelihood corresponding to an IRL-SD problem is N L(θ Ξ σ ) = P(ξ iσ ξ i )P(ξ i θ), i=1 ξ i Ξ ap where we marginalize over the unobserved true ξ i Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

19 Exact solution The likelihood corresponding to an IRL-SD problem is L(θ Ξ σ ) = N i=1 ξ i Ξ ap P(ξ iσ ξ i )P(ξ i θ), where we marginalize over the unobserved true ξ i The set of all plausible true trajectories is Ξ ap S Tmax +1 A Tmax Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

20 Exact solution The likelihood corresponding to an IRL-SD problem is L(θ Ξ σ ) = N i=1 ξ i Ξ ap P(ξ iσ ξ i )P(ξ i θ), where we marginalize over the unobserved true ξ i The set of all plausible true trajectories is Ξ ap S Tmax +1 A Tmax P(ξ iσ ξ i ) is determined by the summary function σ Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

21 Exact solution The likelihood corresponding to an IRL-SD problem is L(θ Ξ σ ) = N i=1 ξ i Ξ ap P(ξ iσ ξ i )P(ξ i θ), where we marginalize over the unobserved true ξ i The set of all plausible true trajectories is Ξ ap S Tmax +1 A Tmax P(ξ iσ ξ i ) is determined by the summary function σ The likelihood of a trajectory is as before T i 1 P(ξ i θ) = P(s0) i πθ (si t, at)p(s i t+1 s i t, i at) i t=0 Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

22 Exact solution The likelihood corresponding to an IRL-SD problem is L(θ Ξ σ ) = N i=1 ξ i Ξ ap P(ξ iσ ξ i )P(ξ i θ), where we marginalize over the unobserved true ξ i The set of all plausible true trajectories is Ξ ap S Tmax +1 A Tmax P(ξ iσ ξ i ) is determined by the summary function σ The likelihood of a trajectory is as before T i 1 P(ξ i θ) = P(s0) i πθ (si t, at)p(s i t+1 s i t, i at) i t=0 Takeaway: L(θ Ξ σ ) can be evaluated, but it is very expensive to do so due to Ξ ap being generally large or challenging to determine Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

23 Monte-Carlo approximation We can estimate L(θ Ξ σ ) by solving π θ and then sampling N MC trajectories, Ξ MC, leading to the Monte-Carlo estimate N ˆL(θ Ξ σ ) = 1 N MC i=1 ξ n Ξ MC P(ξ iσ ξ n ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

24 Monte-Carlo approximation We can estimate L(θ Ξ σ ) by solving π θ and then sampling N MC trajectories, Ξ MC, leading to the Monte-Carlo estimate N ˆL(θ Ξ σ ) = 1 N MC i=1 ξ n Ξ MC P(ξ iσ ξ n ) However P(ξ iσ ξ n ) may be 0 for all ξ n Ξ MC, forcing ˆL(θ Ξ σ ) to be 0 Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

25 Monte-Carlo approximation We can estimate L(θ Ξ σ ) by solving π θ and then sampling N MC trajectories, Ξ MC, leading to the Monte-Carlo estimate ˆL(θ Ξ σ ) N ( 1 i=1 N MC ) P(ξ iσ ξ n )+η ξ n Ξ MC However P(ξ iσ ξ n ) may be 0 for all ξ n Ξ MC, forcing ˆL(θ Ξ σ ) to be 0 (can be fixed with a prior η) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

26 Monte-Carlo approximation We can estimate L(θ Ξ σ ) by solving π θ and then sampling N MC trajectories, Ξ MC, leading to the Monte-Carlo estimate ˆL(θ Ξ σ ) N ( 1 i=1 N MC ) P(ξ iσ ξ n )+η ξ n Ξ MC However P(ξ iσ ξ n ) may be 0 for all ξ n Ξ MC, forcing ˆL(θ Ξ σ ) to be 0 (can be fixed with a prior η) σ needs to be known as a distribution P(ξ iσ ξ n ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

27 Monte-Carlo approximation We can estimate L(θ Ξ σ ) by solving π θ and then sampling N MC trajectories, Ξ MC, leading to the Monte-Carlo estimate ˆL(θ Ξ σ ) N ( 1 i=1 N MC ) P(ξ iσ ξ n )+η ξ n Ξ MC However P(ξ iσ ξ n ) may be 0 for all ξ n Ξ MC, forcing ˆL(θ Ξ σ ) to be 0 (can be fixed with a prior η) σ needs to be known as a distribution P(ξ iσ ξ n ) Takeaway: L(θ Ξ σ ) can be estimated with Monte-Carlo, but there are few technical issues we would like to avoid Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

28 Approximate Bayesian computation ABC also performs inference using on Monte-Carlo sampling Instead of estimating the likelihood of each trajectory ξ i separately, the likelihood of the entire observation set Ξ is estimated together Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

29 Approximate Bayesian computation ABC also performs inference using on Monte-Carlo sampling Instead of estimating the likelihood of each trajectory ξ i separately, the likelihood of the entire observation set Ξ is estimated together How ABC works: Simulate observations using the MC sample: Ξ sim σ = {σ(ξ MC,n )} (only requires us to sample from σ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

30 Approximate Bayesian computation ABC also performs inference using on Monte-Carlo sampling Instead of estimating the likelihood of each trajectory ξ i separately, the likelihood of the entire observation set Ξ is estimated together How ABC works: Simulate observations using the MC sample: Ξ sim σ = {σ(ξ MC,n )} (only requires us to sample from σ) Estimate discrepancy: δ(ξ σ, Ξ sim σ ) [0, ) (matches distributions; reduces effect of individual rare observations) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

31 Approximate Bayesian computation ABC also performs inference using on Monte-Carlo sampling Instead of estimating the likelihood of each trajectory ξ i separately, the likelihood of the entire observation set Ξ is estimated together How ABC works: Simulate observations using the MC sample: Ξ sim σ = {σ(ξ MC,n )} (only requires us to sample from σ) Estimate discrepancy: δ(ξ σ, Ξ sim σ ) [0, ) (matches distributions; reduces effect of individual rare observations) The ε-approximate ABC likelihood: L ε (θ Ξ σ ) = P(δ(Ξ σ, Ξ sim σ ) ε θ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

32 Approximate Bayesian computation ABC also performs inference using on Monte-Carlo sampling Instead of estimating the likelihood of each trajectory ξ i separately, the likelihood of the entire observation set Ξ is estimated together How ABC works: Simulate observations using the MC sample: Ξ sim σ = {σ(ξ MC,n )} (only requires us to sample from σ) Estimate discrepancy: δ(ξ σ, Ξ sim σ ) [0, ) (matches distributions; reduces effect of individual rare observations) The ε-approximate ABC likelihood: L ε (θ Ξ σ ) = P(δ(Ξ σ, Ξ sim σ ) ε θ) Intuition: If simulating observations with θ leads to small prediction error, then likelihood of θ is high and vice versa Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

33 Approximate Bayesian computation ABC also performs inference using on Monte-Carlo sampling Instead of estimating the likelihood of each trajectory ξ i separately, the likelihood of the entire observation set Ξ is estimated together How ABC works: Simulate observations using the MC sample: Ξ sim σ = {σ(ξ MC,n )} (only requires us to sample from σ) Estimate discrepancy: δ(ξ σ, Ξ sim σ ) [0, ) (matches distributions; reduces effect of individual rare observations) The ε-approximate ABC likelihood: L ε (θ Ξ σ ) = P(δ(Ξ σ, Ξ sim σ ) ε θ) Intuition: If simulating observations with θ leads to small prediction error, then likelihood of θ is high and vice versa Takeaway: The issues with MC (numerical problems with rare observations, σ known as a distribution) can be avoided by using ABC Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

34 Inference Now we can estimate L(θ Ξ) at any θ, but how to find the best θ Θ? Evaluating the functions is still expensive The functions don t have accessible gradients Due to limited observability (σ), parameter uncertainty is likely large Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

35 Inference Now we can estimate L(θ Ξ) at any θ, but how to find the best θ Θ? Evaluating the functions is still expensive The functions don t have accessible gradients Due to limited observability (σ), parameter uncertainty is likely large We estimate the log-likelihoods using a GP surrogate model, fit using Bayesian optimization. Mean and shape of distribution estimated from MCMC-samples. Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

36 Inference Now we can estimate L(θ Ξ) at any θ, but how to find the best θ Θ? Evaluating the functions is still expensive The functions don t have accessible gradients Due to limited observability (σ), parameter uncertainty is likely large We estimate the log-likelihoods using a GP surrogate model, fit using Bayesian optimization. Mean and shape of distribution estimated from MCMC-samples. Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

37 Inference Now we can estimate L(θ Ξ) at any θ, but how to find the best θ Θ? Evaluating the functions is still expensive The functions don t have accessible gradients Due to limited observability (σ), parameter uncertainty is likely large We estimate the log-likelihoods using a GP surrogate model, fit using Bayesian optimization. Mean and shape of distribution estimated from MCMC-samples. Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

38 Inference Now we can estimate L(θ Ξ) at any θ, but how to find the best θ Θ? Evaluating the functions is still expensive The functions don t have accessible gradients Due to limited observability (σ), parameter uncertainty is likely large We estimate the log-likelihoods using a GP surrogate model, fit using Bayesian optimization. Mean and shape of distribution estimated from MCMC-samples. Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

39 Simulation experiment We used grid world environments to validate our approach Task was to infer reward weights for state features: R(s) = φ(s) T θ Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

40 Simulation experiment We used grid world environments to validate our approach Task was to infer reward weights for state features: R(s) = φ(s) T θ We only knew the start and end locations of the agent and the length of the trajectory: ξ σ = (s 0, s T, T ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

Simulation experiment We used grid world environments to validate our approach Task was to infer reward weights for state features: R(s) = φ(s) T θ We only knew the start and end locations of the

41 Simulation experiment We used grid world environments to validate our approach Task was to infer reward weights for state features: R(s) = φ(s) T θ We only knew the start and end locations of the agent and the length of the trajectory: ξ σ = (s 0, s T, T ) Miniature example: What kind of terrain might the agent prefer, given that moving from A to B took it T steps? Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

42 Inferred distributions (example) Takeaways The parameter values can be inferred based on summary observations Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

43 Inferred distributions (example) Takeaways The parameter values can be inferred based on summary observations The approximate distributions are similar to the true distribution Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

44 Efficiency Takeaways Summing over all plausible trajectories is expensive with larger MDPs Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

45 Efficiency Takeaways Summing over all plausible trajectories is expensive with larger MDPs The approximate methods scale significantly better Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

46 Accuracy and model fit Takeaways Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

47 Accuracy and model fit Takeaways Good approximation performance while outperforming a random baseline Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

48 Accuracy and model fit Takeaways Good approximation performance while outperforming a random baseline Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

49 Accuracy and model fit Takeaways Good approximation performance while outperforming a random baseline Approximate methods continue performing well even with larger MDPs Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

50 Realistic Experiment We performed experiments using an RL model from cognitive science User searched repeatedly for target items from drop-down menus Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

51 Realistic Experiment We performed experiments using an RL model from cognitive science User searched repeatedly for target items from drop-down menus The MDP contained a simple model of human vision and short-term memory Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

52 Realistic Experiment We performed experiments using an RL model from cognitive science User searched repeatedly for target items from drop-down menus The MDP contained a simple model of human vision and short-term memory Goal: infer values of three model parameters based on observing task completion times (TCT) and whether the target item was present in the menu: ξ σ = (target present?, TCT ) Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

Realistic Experiment We performed experiments using an RL model from cognitive science User searched repeatedly for target items from drop-down menus The MDP contained a simple model of human vision

53 Realistic Experiment We performed experiments using an RL model from cognitive science User searched repeatedly for target items from drop-down menus The MDP contained a simple model of human vision and short-term memory Goal: infer values of three model parameters based on observing task completion times (TCT) and whether the target item was present in the menu: ξ σ = (target present?, TCT ) visual fixation duration f dur item selection duration d sel menu layout recall probability p rec Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

54 Model fit ABC Hold-out data Task Completion Time (abs) 430 ms 470 ms Task Completion Time (pre) 980 ms 970 ms abs = target absent from menu, pre = target present in menu Takeaways Predictions with parameters inferred by ABC match to hold-out observation data, indicating good model fit Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

55 Model fit ABC Hold-out data Task Completion Time (abs) 430 ms 470 ms Task Completion Time (pre) 980 ms 970 ms Number of Saccades (abs) Number of Saccades (pre) abs = target absent from menu, pre = target present in menu Takeaways Predictions with parameters inferred by ABC match to hold-out observation data, indicating good model fit Also unobserved features match approximately to predictions Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

56 Approximate posterior Takeaway Posterior indicates good identification of model parameter values Remaining parameter uncertainty is easy to visualize Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

57 Conclusions We proposed two approximate methods (MC, ABC) for solving the problem of trajectory-level observation noise in IRL More scalable than exact likelihood Good approximation quality Full posterior inference, which is important due to noisy observations Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

58 Conclusions We proposed two approximate methods (MC, ABC) for solving the problem of trajectory-level observation noise in IRL More scalable than exact likelihood Good approximation quality Full posterior inference, which is important due to noisy observations We demonstrated applicability for a realistic cognitive science model based on real observation data Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

59 Conclusions We proposed two approximate methods (MC, ABC) for solving the problem of trajectory-level observation noise in IRL More scalable than exact likelihood Good approximation quality Full posterior inference, which is important due to noisy observations We demonstrated applicability for a realistic cognitive science model based on real observation data Next steps: improve scalability Still requires solving RL problems in the inner loop Scalability of GP and BO to high dimensions Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

60 Conclusions We proposed two approximate methods (MC, ABC) for solving the problem of trajectory-level observation noise in IRL More scalable than exact likelihood Good approximation quality Full posterior inference, which is important due to noisy observations We demonstrated applicability for a realistic cognitive science model based on real observation data Next steps: improve scalability Still requires solving RL problems in the inner loop Scalability of GP and BO to high dimensions More details at the poster tomorrow Antti Kangasrääsiö, Samuel Kaski (Aalto) ECML PKDD 2018 September 12, / 20

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