Misc.

• Stochastic Value Gradient (SVG) (Heess et al., 2015)
• Q-Prop (Gu et al., 2016b)
• Normalized Advantage Function (NAF) (Gu et al., 2016a)
• Fictitious Self-Play (FSP) (Heinrich et al., 2015; Heinrich and Silver, 2016)

Gu, S., Lillicrap, T., Ghahramani, Z., Turner, R. E., and Levine, S. (2016a). Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. Available at: http://arxiv.org/abs/1611.02247.

Gu, S., Lillicrap, T., Sutskever, I., and Levine, S. (2016b). Continuous Deep Q-Learning with Model-based Acceleration. Available at: http://arxiv.org/abs/1603.00748.

Heess, N., Wayne, G., Silver, D., Lillicrap, T., Tassa, Y., and Erez, T. (2015). Learning continuous control policies by stochastic value gradients. Proc. International Conference on Neural Information Processing Systems, 2944–2952. Available at: http://dl.acm.org/citation.cfm?id=2969569.

Heinrich, J., Lanctot, M., and Silver, D. (2015). Fictitious Self-Play in Extensive-Form Games. 805–813. Available at: http://proceedings.mlr.press/v37/heinrich15.html.

Heinrich, J., and Silver, D. (2016). Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. Available at: http://arxiv.org/abs/1603.01121.