Epsilon-greedy
Epsilon-greedy is an action-selection strategy that chooses a random action with probability epsilon and otherwise selects the currently highest-valued action.
Experimentation for Engineers
Epsilon-greedy is a bandit allocation method that usually selects the currently best alternative while exploring alternatives at a specified probability.
- How it is used
- Optimizes business metrics while evaluating production system variants instead of allocating traffic equally.
- How deep it goes
- Defines the decision rule, supplies Python simulations, covers decayed stopping, and generalizes from two alternatives to multiple arms.
- Examples
- Uses ad click models with CTR rewards and a social-media recommender that randomly shows some posts.
- What you can do after
- Apply it via a Python/NumPy-style policy to allocate among alternatives while tracking observed rewards and tuning exploration.
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Optimization Algorithms: AI techniques for design, planning, and control problems
An action-selection strategy that chooses a random action with a specified probability and otherwise selects the currently best-known action.
- How it is used
- Balances exploration and exploitation when selecting actions in multi-armed bandit problems.
- How deep it goes
- Explains the selection rule, loop steps, epsilon parameter, regret calculation, and comparison with alternative bandit strategies.
- Examples
- Uses three slot machines, 90 trials, payoff distributions, NumPy code, and average-regret comparisons.
- What you can do after
- Apply it via NumPy by implementing random exploration, greedy exploitation, payoff sampling, and regret tracking.
Build a Robo-Advisor with Python (From Scratch)
Epsilon-greedy is an action-selection strategy that chooses a random action with probability epsilon and a currently best-known action otherwise.
- How it is used
- Balances exploration and exploitation while Q-learning learns financial-planning actions across simulated episodes.
- How deep it goes
- Explains probabilities, trade-offs, tie-breaking, Q updates, and episode generation, while deferring more sophisticated exploration schedules.
- Examples
- Uses wealth-state transitions, action-value arrays, random tie-breaking, and one million generated episodes.
- What you can do after
- Apply it via Python to generate exploratory Q-learning paths and update action values.
Read how Build a Robo-Advisor with Python (From Scratch) explains epsilon-greedy →
Related concepts
- multi-armed banditaddresses · A multi-armed bandit is a sequential decision problem in which an agent chooses among options with uncertain rewards, balancing reward seeking with gathering information about those options.
- Q-learningused by · Q-learning is a model-free reinforcement-learning algorithm that estimates state-action values through updates based on rewards and subsequent state values.
- Thompson samplingcontrasts with · Thompson sampling is a Bayesian sequential decision method that samples from posterior beliefs about uncertain rewards or outcomes and selects an action according to the sampled values.
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