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1.1.0

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abijith-rl

Reinforcement learning demos and utilities

Published23 May 2026
Direct dependencies15
Transitive dependenciesNot calculated

README

Version 1.1.0 documentation

Abijith RL

Small reinforcement learning demos built on gymnasium.

Install

pip install abijith-rl

Usage

import abijith_rl as rl

mc_df = rl.montecarlo_code(num_episodes=200)
print(mc_df.head())

# Prints the full demo code string
rl.montecarlo()

td_df = rl.td_prediction(num_episodes=50)
print(td_df.head())

sarsa_df = rl.sarsa(num_episodes=200)
print(sarsa_df.head())

q_df = rl.q_learning(num_episodes=200)
print(q_df.head())

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Declared dependencies 15

Native requirements
15 direct targetsNot calculated transitive count
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Distribution files

2 collected
abijith_rl-1.1.0-py3-none-any.whl

bdist_wheel · 10.84 KB · Python py3

Source file
abijith_rl-1.1.0.tar.gz

sdist · 11.27 KB · Python source

Source file

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Observed since 20 Oct, 17:25 UTC

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package_version:9d15cd42377b5c6edb541dcace659f1a94b06dea5c2ad7fe9e46f81101bade83

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