  Inverse Reinforcement Learning (IRL) is a compelling technique for revealing
the rationale underlying the behavior of autonomous agents. IRL seeks to
estimate the unknown reward function of a Markov decision process (MDP) from
observed agent trajectories. However, IRL needs a transition function, and most
algorithms assume it is known or can be estimated in advance from data. It
therefore becomes even more challenging when such transition dynamics is not
known a-priori, since it enters the estimation of the policy in addition to
determining the system's evolution. When the dynamics of these agents in the
state-action space is described by stochastic differential equations (SDE) in
It^{o} calculus, these transitions can be inferred from the mean-field theory
described by the Fokker-Planck (FP) equation. We conjecture there exists an
isomorphism between the time-discrete FP and MDP that extends beyond the
minimization of free energy (in FP) and maximization of the reward (in MDP). We
identify specific manifestations of this isomorphism and use them to create a
novel physics-aware IRL algorithm, FP-IRL, which can simultaneously infer the
transition and reward functions using only observed trajectories. We employ
variational system identification to infer the potential function in FP, which
consequently allows the evaluation of reward, transition, and policy by
leveraging the conjecture. We demonstrate the effectiveness of FP-IRL by
applying it to a synthetic benchmark and a biological problem of cancer cell
dynamics, where the transition function is inaccessible.
