Notes from Ammar/Mocanu – Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines

Citation H. Ammar and D. Mocanu, “Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines,” Mach. Learn. …, 2013. Abstract Existing reinforcement learning approaches are often hampered by learning tabula rasa.  Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned results, but may require an inter-task mapping […]

Training a gated autoencoder

I trained a gated auto-encoder using code mentioned in “Gradient-based learning of higher-order image features” (See: http://www.cs.toronto.edu/~rfm/code/rae/index.html) There are two input layers.  I used 5000 random quadruples <s,a,s’,r> from a MountainCar Task and 5000 from an Inverted Pendulum.  Discrete actions were converted into separate features per action through binarization.  Samples were paired randomly by sorting.  All input […]

Notes from Memisevic – Learning to Relate Images

Citation Memisevic, Roland. “Learning to Relate Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 8 (2013): 1829–1846. doi:10.1109/TPAMI.2013.53. Abstract A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. Recently, there has been […]