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Since the earliest days of reinforcement learning, the workhorse method for assigning credit to actions over time has been temporal-difference (TD) learning, which propagates credit backward timestep-by-timestep.
Dynamic programming
Bellman, R · 1957
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Hochreiter, S. and Schmidhuber, J · 1997
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Human-level control through deep reinforcement learning
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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The pycolab game engine, 2017
Stepleton, T · 2017
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Woulda, coulda, shoulda: Counterfactually-guided policy search
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Rainbow: Combining improvements in deep reinforcement learning
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Unsupervised predictive memory in a goal-directed agent
Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska-Barwinska, A., Rae, J., Mirowski, P., Leibo, J. Z., Santoro, A., et al · 2018
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Self-attentional credit assignment for transfer in reinforcement learning
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Generalization of reinforcement learners with working and episodic memory
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Harutyunyan, A., Dabney, W., Mesnard, T., Azar, M., Piot, B., Heess, N., van Hasselt, H., Wayne, G., Singh, S., Precup, D., et al · 2019
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Optimizing agent behavior over long time scales by transporting value
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Recurrent experience replay in distributed reinforcement learning
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Been there, done that: Meta-learning with episodic recall
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Hung, C.-C., Lillicrap, T., Abramson, J., Wu, Y., Mirza, M., Carnevale, F., Ahuja, A., and Wayne, G · 2019
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Sequence modeling of temporal credit assignment for episodic reinforcement learning
Liu, Y., Luo, Y., Zhong, Y., Chen, X., Liu, Q., and Peng, J · 2019
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The DeepMind JAX Ecosystem, 2020
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Counterfactual credit assignment in model-free reinforcement learning
Mesnard, T., Weber, T., Viola, F., Thakoor, S., Saade, A., Harutyunyan, A., Dabney, W., Stepleton, T., Heess, N., Guez, A., et al · 2020
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