Information Maximizing Exploration with a Latent Dynamics Model

4 Apr 2018 Trevor Barron Oliver Obst Heni Ben Amor

All reinforcement learning algorithms must handle the trade-off between exploration and exploitation. Many state-of-the-art deep reinforcement learning methods use noise in the action selection, such as Gaussian noise in policy gradient methods or $\epsilon$-greedy in Q-learning... (read more)

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