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IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
[article]
2020
arXiv
pre-print
The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process. However, modern methods for scalable reinforcement learning (RL) often tradeoff between the throughput of samples that an RL agent can learn from (sample throughput) and the quality of learning from each sample (sample efficiency). In
arXiv:1912.00167v3
fatcat:onamkixxnnaktfaz2u3vx4qkli