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We are interested in a specific setting of imitation learning—the problem of learning to perform a task from expert demonstrations—in which the learner is given only samples of trajectories from the expert, is not allowed to query the expert for more data while training, and is not provided reinforcement signal of any kind. There are two main approaches suitable for this setting: behavioral cloning [20], which learns a policy as a supervised learning problem over state-action pairs from expert trajectories; and inverse reinforcement learning [25, 18], which finds a cost function under which the expert is uniquely optimal. Behavioral cloning, while appealingly simple, only tends to succeed with large amounts of data, due to compounding error caused by covariate shift [23, 24]. Inverse reinforcement learning (IRL), on the other hand, learns a cost function that prioritizes entire trajectories over others, so compounding error, a problem for methods that fit single-timestep decisions, is not an issue. Accordingly, IRL has succeeded in a wide range of problems, from predicting behaviors of taxi drivers [31] to planning footsteps for quadruped robots [22]. Unfortunately, many IRL algorithms are extremely expensive to run, requiring reinforcement learning in an inner loop. Scaling IRL methods to large environments has thus been the focus of much recent work [7, 14]. Fundamentally, however, IRL learns a cost function, which explains expert behavior but does not directly tell the learner how to act. Given that learner’s true goal often is to take actions imitating the expert—indeed, many IRL algorithms are evaluated on the quality of the optimal actions of the costs they learn—why, then, must we learn a cost function, if doing so possibly incurs significant computational expense yet fails to directly yield actions? We desire an algorithm that tells us explicitly how to act by directly learning a policy. To develop such an algorithm, we begin in Section 3, where we characterize the policy given by running reinforcement learning on a cost function learned by maximum causal entropy IRL [31, 32]. Our characterization introduces a framework for directly learning policies from data, bypassing any intermediate IRL step. Then, we instantiate our framework in Sections 4 and 5 with a new model-free imitation learning algorithm. We show that our resulting algorithm is intimately connected to generative adversarial networks [9], a technique from the deep learning community that has led to recent successes in modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where we find that it outperforms competing methods by a wide margin in training policies for complex, high-dimensional physics-based control tasks over various amounts of expert data.