Reinforcement Learning
Reliable Decision Support using Counterfactual Models
One use of such an estimate is to evaluate risk; e.g. is this patient likely to die if I do not
Multi-View Decision Processes: The Helper-AI Problem
Christos Dimitrakakis, David C. Parkes, Goran Radanovic, Paul Tylkin
We model this setting as a multi-view decision process, which we use to formally analyze the positive effect of steering policies.
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
Taylor W. Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez
We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings.
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Justin Fu, John Co-Reyes, Sergey Levine
The intuition is that, if a state is easy to distinguish from other states, it is likely to be novel.
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann, Tor Lattimore, Emma Brunskill
Tor Lattimore is now at DeepMind, London 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Uniform-P AC algorithms suffer none of these drawbacks.
Hybrid Reward Architecture for Reinforcement Learning Harm van Seijen
One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dimensional representation using a deep network.
Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M. Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu
In this section we describe the mathematical framework underlying Distral.
State Aware Imitation Learning
Yannick Schroecker, Charles L. Isbell
Formally, we define the problem domain as a Markov decision process, i.e. by its states, actions and unknown Markovian transition probabilities
Verifiable Reinforcement Learning via Policy Extraction
Osbert Bastani, Yewen Pu, Armando Solar-Lezama
DNNs, verifying these properties is typically very inefficient if not infeasible [6].
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.