24 citations · 73 across the 7 of their papers we have counts for
7 papers
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban
Peter Karkus, Mehdi Mirza, Arthur Guez +5
Intelligent robots need to achieve abstract objectives using concrete, spatiotemporally complex sensory information and motor control. Tabula rasa deep reinforcement learning (RL)…
Causally Correct Partial Models for Reinforcement Learning
Danilo J. Rezende, Ivo Danihelka, George Papamakarios +11
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can b…
Combining Q-Learning and Search with Amortized Value Estimates
Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez +4
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned pri…
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions
Lars Buesing, Nicolas Heess, Theophane Weber
A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive,…
Unsupervised Doodling and Painting with Improved SPIRAL
John F. J. Mellor, Eunbyung Park, Yaroslav Ganin +7
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is…
Credit Assignment Techniques in Stochastic Computation Graphs
Théophane Weber, Nicolas Heess, Lars Buesing +1
Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and r…