Behavioral Cloning (BC)¶
Paper |
Model-Free Imitation Learning with Policy Optimization [1] |
Framework(s) |
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API Reference |
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Code |
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Examples |
Behavioral cloning is a simple immitation learning algorithm which maxmizes the likelhood of an expert demonstration’s actions under the apprentice policy using direct policy optimization. Garage’s implementation may use either a policy or dataset as the expert.
Default Parameters¶
policy_optimizer = torch.optim.Adam
policy_lr = 1e-3
loss = 'log_prob'
batch_size = 1000
References¶
- 1
Jonathan Ho, Jayesh Gupta, and Stefano Ermon. Model-free imitation learning with policy optimization. In International Conference on Machine Learning, 2760–2769. 2016. URL: https://arxiv.org/abs/1605.08478.
This page was authored by Iris Liu (@irisliucy) with contributions from Ryan Julian (@ryanjulian).