# How Data Scientists Use Reinforcement Learning for Dynamic Pricing Page: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-reinforcement-learning-for-dynamic-pricing Text version: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-reinforcement-learning-for-dynamic-pricing.md Podcast: [The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations](https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831) Published: 2026-06-15T08:20:53+00:00 Episode link: https://audio.fexingo.com/business/the-data-science-podcast/episode-0052.mp3 Audio file: https://audio.fexingo.com/business/the-data-science-podcast/episode-0052.mp3 Processing state: not_requested JSON: https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-reinforcement-learning-for-dynamic-pricing Duration seconds: 548 ## Resource In this episode of The Data Science Podcast, Lucas and Luna explore how reinforcement learning (RL) is transforming dynamic pricing strategies. Using the example of a major ride-hailing company, they break down how RL algorithms learn to set prices in real time by balancing exploration (testing new price points) and exploitation (using known optimal prices). Lucas explains the core RL concepts of state, action, reward, and the epsilon-greedy algorithm. Luna digs into the practical trade-offs: how often should a model explore versus exploit, and why the reward function must account for long-term customer retention, not just immediate revenue. The conversation also touches on how RL differs from A/B testing in dynamic pricing, the role of simulation environments for training, and ethical considerations around price discrimination. Listeners will walk away with a concrete understanding of RL-based pricing mechanics and a mental model to evaluate pricing algorithms they encounter daily. #ReinforcementLearning #DynamicPricing #DataScience #MachineLearning #RL #PricingStrategy #RideHailing #ExplorationExploitation #EpsilonGreedy #RewardFunction #AIBusiness #Technology #TechPodcast #DataDriven #FexingoBusiness #BusinessPodcast #DataSciencePodcast #Fexingo Keep every episode free: buymeacoffee.com/fexingo ## Actions - request_transcript: `POST https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-reinforcement-learning-for-dynamic-pricing/transcription-requests` — Idempotently request low-priority transcript generation for this episode. - read_markdown: `GET https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-reinforcement-learning-for-dynamic-pricing.md` — Read the agent-friendly Markdown representation of this episode resource. A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed. ## Transcript Full transcripts are not published on public pages unless there is a clear rights basis.