<ul><li>Parallel actors for data collection have been effective in reinforcement learning algorithms.</li><li>Data collection methods in RL algorithms induce bias-variance trade-off.</li><li>Empirical analysis on PPO algorithm with parallel actors shows impact on network architectures and optimization stability.</li><li>Scaling parallel environments is more effective than increasing rollout lengths in improving agent performance.</li></ul>

The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks

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