Metadata-Version: 2.4
Name: simple-rl-pack-Kritarth04
Version: 0.0.1
Summary: A very simple Epsilon-Greedy Bandit RL example package
Author-email: Kritarth Ghosh <kritarthg04@gmail.com>
Project-URL: Homepage, https://github.com/your-username/my_rl_project
Project-URL: Bug Tracker, https://github.com/your-username/my_rl_project/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# Simple RL Pack

A simple Python package demonstrating an Epsilon-Greedy Multi-Armed Bandit.

This package was created as an individual activity.

## Usage

\`\`\`python
from simple_rl_pack import EpsilonGreedyBandit

# Create a bandit with 3 arms
bandit = EpsilonGreedyBandit(num_arms=3, epsilon=0.1)

# --- Simulate pulling arms ---

# 1. Select an action
action_to_pull = bandit.select_action()

# 2. Get a reward (in a real app, this comes from your environment)
# Let's pretend arm 1 is the best
reward = 0.0
if action_to_pull == 1:
    reward = 1.0
elif action_to_pull == 2:
    reward = 0.5

# 3. Update the bandit's knowledge
bandit.update_value(action_to_pull, reward)

print(f"Pulled arm {action_to_pull}, received reward {reward}")
print(f"Current best guess: Arm {bandit.get_best_arm()}")
\`\`\`
