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One weakness of coevolutionary algorithms observed in knowledge-free learning of strategies for adversarial games has been their poor scalability with respect to the number of parameters to learn. In this paper, we investigate to what extent this problem can be mitigated by using Covariance Matrix Adaptation Evolution Strategy, a powerful continuous optimization algorithm. In particular, we employ...
Deep Q-Learning is an effective reinforcement learning method, which has recently obtained human-level performance for a set of Atari 2600 games. Remarkably, the system was trained on the high-dimensional raw visual data. Is Deep Q-Learning equally valid for problems involving a low-dimensional state space? To answer this question, we evaluate the components of Deep Q-Learning (deep architecture,...
The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement...
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