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thomson-sampling

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kochlisGit / Reinforcement-Learning-Algorithms

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This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.

python reinforcement-learning monte-carlo openai-gym q-learning policy rl-agents epsilon-greedy dynamic-programming markov-chains approximation-algorithms ucb1 q-lambda exploration-exploitation thomson-sampling frozen-lake multi-bandit-army
  • Updated Feb 15, 2022
  • Python

amritanshuroy28 / SpeakX_AURORA

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Autonomously optimizes B2C notifications through intelligent segmentation (MECE + behavioral scoring), LLM-powered content generation (Hinglish templates via Groq), and adaptive timing optimization. Uses Thompson Sampling to learn from every iteration with full causal audit trails—no mock learning, real measurable delta.

python machine-learning optimization jupyter-notebook personalization behavioral-analytics thomson-sampling notification-orchestrator-service
  • Updated Mar 10, 2026
  • Jupyter Notebook

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