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Distributional Reinforcement Learning with Quantile Regression
Will Dabney, Mark Rowland, Marc G. Bellemare +1
In reinforcement learning an agent interacts with the environment by taking actions and observing the next state and reward. When sampled probabilistically, these state transitions…
The Uncertainty Bellman Equation and Exploration
Brendan O'Donoghue, Ian Osband, Remi Munos +1
We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the…
A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Will Dabney, Rémi Munos
In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contra…
Observational Learning by Reinforcement Learning
Diana Borsa, Bilal Piot, Rémi Munos +1
Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. It is a core me…
Noisy Networks for Exploration
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot +9
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to…
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Marc G. Bellemare, Ivo Danihelka, Will Dabney +4
The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probab…