Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments

Researchers have identified that performance stagnation in Proximal Policy Optimization (PPO) is due to poor loss estimation over time rather than exploration or optimization issues. They found increasing the number of parallel environments can mitigate this problem and demonstrated significant improvements by scaling PPO to more than 1 million parallel environments, allowing for continuous performance enhancement. Content creators should consider scaling up parallel environments as a robust method to enhance model training efficiency.

Read the full article at arXiv cs.LG (ML)

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