semvec/pypi-packages huggingface dataset:
decisionbandit 0.1.1
A collection of exploration–exploitation strategies for reinforcement learning, including ε-greedy and related policies
License: MIT
Requires Python: >=3.8

# epsilon-policies

A collection of explorationâ€“exploitation strategies for reinforcement learning, including Îµ-greedy and related policies.

## Features
- **Exploration** â€“ Select random actions to discover new possibilities.
- **Exploitation** â€“ Choose the best-known action based on current estimates.
- **Fixed Explorationâ€“Thenâ€“Exploitation** â€“ Explore for a fixed period, then fully exploit.
- **Îµ-Greedy** â€“ Balance exploration and exploitation with a probability parameter.
- **Îµ-Greedy with UCB** â€“ Enhance Îµ-greedy with Upper Confidence Bound for better action selection.

## Installation


```bash
pip install decisionbandit


import decisionbandit as dcb

# Example: Îµ-greedy

reward_prob=[0.2,0.3,0.4]
action = dcb.epsilon_greedy(reward_prob,epsilon=0.1, n_arms=3,t_steps=100)
print("Selected action:", action)


MIT License