Pre-Estimation Checks

Read this page before fitting AIRL. The most important check is whether the reward object you want is actually state-only, because that is where the original recovery guarantee applies.

Adversarial training is sensitive to a small number of setup choices. Run these checks before fitting.

Check	Why it matters for AIRL
reward_arg vs DGP signal	If the DGP puts its signal in action contrasts, state-only AIRL cannot match it. Set reward_arg="state_action" when payoffs are action-dependent.
Discriminator loss near log(2)	A final discriminator loss well above log(2) (about 0.693) suggests the game did not converge. Inspect before interpreting reward metrics.
max_rounds adequate	Too few rounds leave the game undertrained; policy TV stays high. Typical good-fit runs use 150-300 rounds.
Feature rank	A rank-deficient reward feature matrix leaves directions of the reward undetermined.
Feature condition number	High condition numbers (above ~100) can slow discriminator convergence.
State coverage	States not seen in the panel do not contribute discriminator gradients. The policy at those states is only informed by the Bellman propagation from visited neighbors.
Action support per state	A state where one action is never taken means the discriminator never gets a gradient signal for that (state, action) pair.
Transition row sums	Transitions must be row-stochastic in the (n_actions, n_states, n_states) orientation.

Simulation Cell Checks

Values from the primary state-only simulation cell (see Simulation Study):

Check	Value	Status
Feature rank	4 / 4	pass
Feature condition number	62.58	pass
Observed states	16 / 16	pass
State-action coverage	1.000	pass
Minimum action share	0.242	pass
Final discriminator loss	1.386	near log(2)

The condition number of 62.58 is moderate for a state-only linear reward and does not impair convergence here, but it is worth noting before applying AIRL to a different feature basis.

Common Risk Patterns

Wrong reward_arg. When the DGP signal is entirely in action contrasts and reward_arg="state" is used, the reward is averaged across actions before computing the discriminator logit. Both actions receive equal flow utility; the policy collapses toward uniform and policy TV stays high regardless of training length. The fix is reward_arg="state_action" plus a longer schedule.

Undertrained game. With default max_rounds=200, very short runs (below 100 rounds) often leave the discriminator loss above log(2) and the policy far from the oracle. Increase max_rounds and monitor final_disc_loss.

Interpreting raw parameters. Raw adversarial reward weights are not structural parameters. Reward is identified only up to potential-based shaping, so parameter-level comparisons with the data-generating truth are not meaningful. Policy TV and counterfactual regret are the right scorecards.