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Reinforcement learning explained: policies, MDPs, and trajectories

By PulseAugur Editorial · [1 sources] ·

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

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]

Read on Mastodon — sigmoid.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reinforcement learning explained: policies, MDPs, and trajectories

COVERAGE [1]

  1. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    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…

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