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Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore generalizes poorly to unseen states.
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., and Levine, S · 2018
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Kurutach, T., Clavera, I., Duan, Y., Tamar, A., and Abbeel, P · 2018
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Nagabandi, A., Kahn, G., Fearing, R. S., and Levine, S · 2018
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Intervention design for effective sim2real transfer
Mozifian, M., Zhang, A., Pineau, J., and Meger, D · 2020
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Volodin, S., Wichers, N., and Nixon, J · 2020
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On the model-based stochastic value gradient for continuous reinforcement learning
Amos, B., Stanton, S., Yarats, D., and Wilson, A. G · 2021
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Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., and Schölkopf, B · 2019
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Contrastive learning of structured world models
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Sub-policy adaptation for hierarchical reinforcement learning
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Learning causal state representations of partially observable environments
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Object files and schemata: Factorizing declarative and procedural knowledge in dynamical systems
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Neural production systems
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Systematic evaluation of causal discovery in visual model based reinforcement learning
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Resolving causal confusion in reinforcement learning via robust exploration
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Necessary and sufficient conditions for causal feature selection in time series with latent common causes
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Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
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Causal influence detection for improving efficiency in reinforcement learning
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Causal curiosity: Rl agents discovering self-supervised experiments for causal representation learning
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Model-invariant state abstractions for model-based reinforcement learning
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Task-independent causal state abstraction
Wang, Z., Xiao, X., Zhu, Y., and Stone, P · 2021
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