Reinforcement learning explained: policies, MDPs, and trajectories

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 1 source

This article explains how reinforcement learning agents make decisions by defining key concepts. It covers policies, Markov Decision Processes (MDPs), and trajectories. The series aims to build understanding towards the Proximal Policy Optimization (PPO) algorithm. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Explains fundamental concepts in reinforcement learning, crucial for understanding agent behavior and advanced algorithms.

RANK_REASON Educational content explaining core concepts in a machine learning subfield. [lever_c_demoted from research: ic=1 ai=1.0]

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Mastodon — sigmoid.social TIER_1 · [email protected] · 2026-05-19 17:30

How does a # ReinforcementLearning agent decide what to do? Part 3 of my RL series tackles this by defining policies, MDPs and trajectories. We'll keep building

How does a # ReinforcementLearning agent decide what to do? Part 3 of my RL series tackles this by defining policies, MDPs and trajectories. We'll keep building up to fully grasping PPO! https:// shawnhymel.com/3328/reinforcem ent-learning-part-3-policies-markov-decision-processe…

LINKS shawnhymel.com/…/reinforcement-learning-p… shawnhymel.com/…/reinforcement-learning-p…

COVERAGE [1]

How does a # ReinforcementLearning agent decide what to do? Part 3 of my RL series tackles this by defining policies, MDPs and trajectories. We'll keep building

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