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Adds citation to Baird94 (#256)

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Rafael Cosman
2016-07-20 18:02:53 -07:00
committed by jietang
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gym/scoreboard/__init__.py
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@@ -1223,9 +1223,12 @@ The goal of the agent is to maximize the true reward function given just the noi
Prior work has explored learning algorithms for human training scenarios of this flavor [Lopes11]_. Prior work has explored learning algorithms for human training scenarios of this flavor [Lopes11]_.
Additionally, Baird and others have noted the relationship between update noise, timestep size, and convergence rate for Q-learners [Baird94]_.
Robustness to noisy rewards may aid scalable oversight in settings where evaluating Robustness to noisy rewards may aid scalable oversight in settings where evaluating
the true reward signal is expensive or impossible but a noisy approximation is available [Amodei16]_, [Christiano15]_. the true reward signal is expensive or impossible but a noisy approximation is available [Amodei16]_, [Christiano15]_.
.. [Baird94] Baird, Leemon C. "Reinforcement learning in continuous time: Advantage updating." Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on. Vol. 4. IEEE, 1994.
.. [Amodei16] Amodei, Olah, et al. `"Concrete Problems in AI safety" Arxiv. 2016. <https://arxiv.org/pdf/1606.06565v1.pdf>`_ .. [Amodei16] Amodei, Olah, et al. `"Concrete Problems in AI safety" Arxiv. 2016. <https://arxiv.org/pdf/1606.06565v1.pdf>`_
.. [Lopes11] Lopes, Manuel, Thomas Cederbourg, and Pierre-Yves Oudeyer. "Simultaneous acquisition of task and feedback models." Development and Learning (ICDL), 2011 IEEE International Conference on. Vol. 2. IEEE, 2011. .. [Lopes11] Lopes, Manuel, Thomas Cederbourg, and Pierre-Yves Oudeyer. "Simultaneous acquisition of task and feedback models." Development and Learning (ICDL), 2011 IEEE International Conference on. Vol. 2. IEEE, 2011.
.. [Christiano15] `AI Control <https://medium.com/ai-control/>`_ .. [Christiano15] `AI Control <https://medium.com/ai-control/>`_