Deep Inverse Q-learning with Constraints Appendix Gabriel Kalweit

Visualizations of the real and learned state-values of IA VI, IQL and DIQL can be found in Figure 7.Figure 7: Visualization of state-values for different numbers of trajectories in Objectworld. Table 2: Comparison between online and offline estimation of state-action visitations for the Ob-jectworld environment, given a data set with an action distribution equivalent to the true optimal Boltzmann distribution. The pseudocode of the tabular variant of Constrained Inverse Q-learning can be found in Algorithm 4. See [4] for further details of Constrained Q-learning.Algorithm 4: Tabular Model-free Constrained Inverse Q-learning The pseudocode of Deep Constrained Inverse Q-learning can be found in Algorithm 5. The lower row shows the EVD. 3 For DIQL, the parameters were optimized in the range of Hence, it can only increase.