Reinforcement Learning and Bandits in Plain Language
Reinforcement Learning & Decision Optimization: leave with a Q-learning agent on a toy environment and a bandit simulation with exploration analysis.
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why reinforcement learning & decision optimization matters — the common problem: RL ideas behind RLHF and recommendations stay abstract without hands-on examples.
- 2Core concepts in plain language: States, actions, rewards, policies, value functions, Markov decision processes
- 3Going further: exploration versus exploitation, Q-learning, policy gradients, actor–critic methods, contextual bandits, imitation learning
- 4Framework walkthrough: CRISP-DM, Train / Validation / Test Splits, Cross-Validation
- 5Practical workflow, built live: A Q-learning agent on a toy environment and a bandit simulation with exploration analysis.
- 6How to measure it: Accuracy, precision and recall, Generalization gap, Baseline comparison
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A Q-learning agent on a toy environment and a bandit simulation with exploration analysis.
- A working understanding of CRISP-DM and Train / Validation / Test Splits
- A short list of measures to track: Accuracy, precision and recall, Generalization gap, Baseline comparison
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