# awjuliani/DeepRL-Agents

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

Repository: https://github.com/awjuliani/DeepRL-Agents
Canonical: https://ross.abutalabs.com/products/deeprl-agents
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: reinforcement-learning, tensorflow
Last push: 2019-02-12T17:26:26+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3732, "days_push": 2759, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2279, forks 820 (observed 2026-08-28T04:06:33.725396+00:00)

## What it is
A collection of deep reinforcement learning agents implemented in TensorFlow as Jupyter notebooks, covering algorithms from Q-tables to A3C. It accompanies a Medium tutorial series and serves as an educational resource for learning RL.

## Use cases
- learn reinforcement learning from scratch with worked examples
- understand how DQN and double dueling DQN work
- study policy gradient methods like vanilla policy networks
- see an A3C implementation for 3D environments like VizDoom
- explore exploration strategies such as Boltzmann and Bayesian dropout
- follow a tutorial series on deep RL with TensorFlow

## When to choose
- you are learning deep RL and want readable, notebook-based implementations
- you want code paired with explanatory tutorial articles
- you need reference implementations of classic RL algorithms in TensorFlow 1.x

## When to avoid
- you need a production-ready or maintained RL library
- you use modern TensorFlow 2.x or PyTorch
- you need the latest RL algorithms like PPO or SAC

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, reinforcement-learning
- domain: reinforcement-learning, machine-learning, tutorials
- platform: python
- tags: tensorflow, jupyter-notebooks, q-learning, policy-gradients, a3c, dqn, tutorial-series

## Member repositories
- awjuliani/DeepRL-Agents (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:33.725396+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:41:23.314049+00:00, confidence not recorded.
  - readme: https://github.com/awjuliani/DeepRL-Agents (fetched 2026-08-28T04:06:33.725396+00:00, sha 10be56c344f0)
- Data as of 2026-08-30T08:39:29.467469+00:00.