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2022

Behavioral Cloning via Search in Video PreTraining Latent Space

Malato, Federico, Leopold, Florian, Raut, Amogh et al.

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Our aim is to build autonomous agents that can solve tasks in environments like Minecraft.

To do so, we used an imitation learning-based approach.
We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs.
We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model.

Behavioral Cloning via Search in Video PreTraining Latent Space · Around

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L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, S. Legg, and K. Kavukcuoglu, “IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures,” CoRR
Original
2018
Earlier work this paper cites.
A. Melnik, F. Schüler, C. A. Rothkopf, and P. König, “The world as an external memory: the price of saccades in a sensorimotor task,” Frontiers in behavioral neuroscience
2018
Earlier work this paper cites.
T. Korthals, M. Hesse, J. Leitner, A. Melnik, and U. Rückert, “Jointly trained variational autoencoder for multi-modal sensor fusion,” in 2019 22th International Conference on Information Fusion (FUSION)
2019
Earlier work this paper cites.
A. Melnik, S. Fleer, M. Schilling, and H. Ritter, “Modularization of end-to-end learning: Case study in arcade games,” arXiv preprint arXiv:1901.09895
Original
2019
Earlier work this paper cites.

Similar

K. Konen, T. Korthals, A. Melnik, and M. Schilling, “Biologically-inspired deep reinforcement learning of modular control for a six-legged robot,” in 2019 IEEE International Conference on Robotics and Automation Workshop on Learning Legged Locomotion Workshop,(ICRA) 2019, Montreal, CA, May 20-25, 2019
2019
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N. Bach, A. Melnik, M. Schilling, T. Korthals, and H. Ritter, “Learn to move through a combination of policy gradient algorithms: Ddpg, d4pg, and td3,” in International Conference on Machine Learning, Optimization, and Data Science
2020
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R. Shah, C. Wild, S. H. Wang, N. Alex, B. Houghton, W. H. Guss, S. P. Mohanty, A. Kanervisto, S. Milani, N. Topin, P. Abbeel, S. Russell, and A. D. Dragan, “The minerl BASALT competition on learning from human feedback,” CoRR
Original
2021
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M. Schilling, A. Melnik, F. W. Ohl, H. J. Ritter, and B. Hammer, “Decentralized control and local information for robust and adaptive decentralized deep reinforcement learning,” Neural Networks

Then

A. Melnik, A. Harter, C. Limberg, K. Rana, N. Sünderhauf, and H. Ritter, “Critic guided segmentation of rewarding objects in first-person views,” in German Conference on Artificial Intelligence (Künstliche Intelligenz)
2021
Later among the works it cites.
S. Beohar and A. Melnik, “Planning with rl and episodic-memory behavioral priors,” arXiv preprint arXiv:2207.01845
Original
2022
Closest in time.
S. Beohar, F. Heinrich, R. Kala, H. Ritter, and A. Melnik, “Solving learn-to-race autonomous racing challenge by planning in latent space,” arXiv preprint arXiv:2207.01275
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2022
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B. Baker, I. Akkaya, P. Zhokhov, J. Huizinga, J. Tang, A. Ecoffet, B. Houghton, R. Sampedro, and J. Clune, “Video pretraining (vpt): Learning to act by watching unlabeled online videos,” arXiv preprint arXiv:2206.11795
Original
2022
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