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RLT
Athmapriyan/RLT
RLT is a machine learning model from Athmapriyan. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Just open index.html in any modern browser. No build step, no server needed.
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Public
Updated Apr 3, 2026
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.js14.6 KB · 35%
At a glance
- Access
- Public
- Created
- Apr 3, 2026
- Updated
- Apr 3, 2026
- SHA
- ff3aa9ba
Model card
From the Hugging Face model README
The Effect of Optimism in the Face of Uncertainty
A Study of Optimistic Initialization in Multi-Armed Bandits
Project Structure
bandit-project/
├── index.html ← Main page (hero, theory, simulator, insights)
├── style.css ← All styles (dark theme, responsive layout)
├── simulation.js ← Core bandit engine (BanditEnv, EpsilonGreedyAgent, runSimulation)
└── main.js ← UI logic (Plotly charts, sliders, hero canvas)
How to Run
Just open index.html in any modern browser. No build step, no server needed.
# Option 1: Direct
open index.html
# Option 2: Local server (recommended)
python3 -m http.server 8080
# then visit http://localhost:8080
Algorithm
ε-Greedy with three initialization strategies:
| Strategy | Initial Q₀ | Exploration drive |
|---|---|---|
| Optimistic | +5 to +20 | High (natural) |
| Normal | 0 | Medium (ε only) |
| Pessimistic | −5 to −20 | Low |
Update rule: Q(a) ← Q(a) + [R − Q(a)] / N(a) (incremental sample mean)
Parameters
| Parameter | Description | Range |
|---|---|---|
| K (arms) | Number of bandit arms | 2 – 10 |
| Iterations | Steps per simulation run | 100 – 3000 |
| ε (epsilon) | Exploration probability | 0.00 – 0.50 |
| Runs | Repetitions averaged together | 1 – 50 |
| σ (noise) | Reward standard deviation | 0.1 – 3.0 |
| Opt Q₀ | Optimistic initial value | +1 to +20 |
| Pess Q₀ | Pessimistic initial value | −1 to −20 |
Metrics Tracked
- Average reward over iterations (smoothed with rolling window)
- % Optimal action selected — how fast the agent finds the best arm
- Cumulative reward — total reward accumulated
- Arm selection heatmap — which arms each strategy visited most
Key Takeaways
- Optimism = free exploration — even with ε=0, optimistic agents explore all arms
- Faster convergence — optimistic agents discover the best arm sooner
- Pessimism is costly — premature commitment to a suboptimal arm
- ε still matters — for non-stationary environments, keep ε > 0
sanjay
- Plotly.js — interactive charts
- Google Fonts — Syne + Space Mono
- Created
- Apr 3, 2026
- Updated
- Apr 3, 2026