Generative Adversarial Imitation Learning
Jonathan Ho
OpenAI
hoj@openai.com
Stefano Ermon
Stanford University
ermon@cs.stanford.edu
Abstract
Consider learning a policy from example expert behavior, without interaction with
the expert or access to a reinforcement signal. One approach is to recover the
expert’s cost function with inverse reinforcement learning, then extract a policy
from that cost function with reinforcement learning. This approach is indirect
and can be slow. We propose a new general framework for directly extracting a
policy from data as if it were obtained by reinforcement learning following inverse
reinforcement learning. We show that a certain instantiation of our framework
draws an analogy between imitation learning and generative adversarial networks,
from which we derive a model-free imitation learning algorithm that obtains signif-
icant performance gains over existing model-free methods in imitating complex
behaviors in large, high-dimensional environments.
1 Introduction
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 a
reinforcement signal of any kind. There are two main approaches suitable for this setting: behavioral
cloning [
18
], which learns a policy as a supervised learning problem over state-action pairs from
expert trajectories; and inverse reinforcement learning [
23
,
16
], 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 [
21
,
22
]. 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 [
29
] to planning
footsteps for quadruped robots [20].
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 [
6
,
13
]. Fundamentally, however, IRL learns a cost function, which explains expert behavior
but does not directly tell the learner how to act. Given that the 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 [
29
,
30
]. 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
30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain.
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