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exploration-in-rl

topic1 events
papersTODAY 04:00 UTC

Paper Analyzes How Exploration Emerges in Policy Gradient RL Through Retried States

A revised arXiv paper examines why exploration helps in reinforcement learning, arguing it only pays off when agents revisit similar states repeatedly. The authors show that without such retries, a purely greedy policy would be optimal, and study how exploration behavior can emerge in policy gradient methods.

reinforcement-learning/exploration/research/policy-gradient