This Paper from Google DeepMind Explores Sparse Training: A Game-Changer in Machine Learning Efficiency for Reinforcement Learning Agents

The efficacy of deep reinforcement learning (RL) agents critically depends on their ability to utilize network parameters efficiently. Recent insights have cast light on deep RL agents’ challenges, notably their tendency to underutilize network parameters, leading to suboptimal performance. This inefficiency is not merely a technical hiccup but a fundamental bottleneck that curtails the potential […]

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