Exploration Strategies in Deep Reinforcement Learning
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- Jul 21, 2026
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[Updated on 2020-06-17: Add “exploration via disagreement” in the “Forward Dynamics” section . Exploitation versus exploration is a critical topic in Reinforcement Learning. We’d like the RL agent to find the best solution as fast as possible. However, in the meantime, committing to solutions too quickly without enough exploration sounds pretty bad, as it could lead to local minima or total failure. Modern RL algorithms that optimize for the best returns can achieve
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A structured survey of modern exploration methods in deep RL — count-based bonuses, curiosity/forward-dynamics, and exploration via disagreement — giving agent builders a concrete map of techniques to combat premature convergence to local minima.
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