16 citations · 86 across the 20 of their papers we have counts for
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Learning to Represent Action Values as a Hypergraph on the Action Vertices
Arash Tavakoli, Mehdi Fatemi, Petar Kormushev
Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value c…
Exploring Restart Distributions
Arash Tavakoli, Vitaly Levdik, Riashat Islam +2
We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those cor…
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Fabio Pardo, Vitaly Levdik, Petar Kormushev
Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often no…
Goal-oriented Trajectories for Efficient Exploration
Fabio Pardo, Vitaly Levdik, Petar Kormushev
Exploration is a difficult challenge in reinforcement learning and even recent state-of-the art curiosity-based methods rely on the simple epsilon-greedy strategy to generate novel…
Time Limits in Reinforcement Learning
Fabio Pardo, Arash Tavakoli, Vitaly Levdik +1
In reinforcement learning, it is common to let an agent interact for a fixed amount of time with its environment before resetting it and repeating the process in a series of episod…
Action Branching Architectures for Deep Reinforcement Learning
Arash Tavakoli, Fabio Pardo, Petar Kormushev
Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requir…