• Corpus ID: 16153365

Generative Adversarial Imitation Learning

@inproceedings{Ho2016GenerativeAI,
  title={Generative Adversarial Imitation Learning},
  author={Jonathan Ho and Stefano Ermon},
  booktitle={NIPS},
  year={2016}
}
Consider learning a policy from example expert behavior, without interaction with the expert or access to 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… 

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References

SHOWING 1-10 OF 33 REFERENCES

Model-Free Imitation Learning with Policy Optimization

Under the apprenticeship learning formalism, this work develops alternative model-free algorithms for finding a parameterized stochastic policy that performs at least as well as an expert policy on an unknown cost function, based on sample trajectories from the expert.

Efficient Reductions for Imitation Learning

This work proposes two alternative algorithms for imitation learning where training occurs over several episodes of interaction and shows that this leads to stronger performance guarantees and improved performance on two challenging problems: training a learner to play a 3D racing game and Mario Bros.

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

This paper proposes a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting and demonstrates that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.

Maximum Entropy Inverse Reinforcement Learning

A probabilistic approach based on the principle of maximum entropy that provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods is developed.

Apprenticeship learning via inverse reinforcement learning

This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.

Nonlinear Inverse Reinforcement Learning with Gaussian Processes

A probabilistic algorithm that allows complex behaviors to be captured from suboptimal stochastic demonstrations, while automatically balancing the simplicity of the learned reward structure against its consistency with the observed actions.

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

This work explores how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems and an efficient sample-based approximation for MaxEnt IOC.

Generative Adversarial Nets

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a

Continuous Inverse Optimal Control with Locally Optimal Examples

A probabilistic inverse optimal control algorithm that scales gracefully with task dimensionality, and is suitable for large, continuous domains where even computing a full policy is impractical.

Learning to search: Functional gradient techniques for imitation learning

The work presented extends the Maximum Margin Planning (MMP) framework to admit learning of more powerful, non-linear cost functions, and demonstrates practical real-world performance with three applied case-studies including legged locomotion, grasp planning, and autonomous outdoor unstructured navigation.