58 citations · 131 across the 7 of their papers we have counts for
9 papers
Estimating -Rank by Maximizing Information Gain
Tabish Rashid, Cheng Zhang, Kamil Ciosek
Game theory has been increasingly applied in settings where the game is not known outright, but has to be estimated by sampling. For example, meta-games that arise in multi-agent e…
Regularized Policies are Reward Robust
Hisham Husain, Kamil Ciosek, Ryota Tomioka
Entropic regularization of policies in Reinforcement Learning (RL) is a commonly used heuristic to ensure that the learned policy explores the state-space sufficiently before overf…
Evaluating the Robustness of Collaborative Agents
Paul Knott, Micah Carroll, Sam Devlin +4
In order for agents trained by deep reinforcement learning to work alongside humans in realistic settings, we will need to ensure that the agents are \emph{robust}. Since the real…
DRIFT: Deep Reinforcement Learning for Functional Software Testing
Luke Harries, Rebekah Storan Clarke, Timothy Chapman +10
Efficient software testing is essential for productive software development and reliable user experiences. As human testing is inefficient and expensive, automated software testing…
Discount Factor as a Regularizer in Reinforcement Learning
Ron Amit, Ron Meir, Kamil Ciosek
Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by a discount factor. It is known that applying RL algorithm…
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li +4
The ability for policies to generalize to new environments is key to the broad application of RL agents. A promising approach to prevent an agent's policy from overfitting to a lim…