# p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

Repository: https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
Canonical: https://ross.abutalabs.com/products/deep-reinforcement-learning-algorithms-with-pytorch
Language: Python
License: MIT
License Family: permissive
Last push: 2024-07-25T10:14:35+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2917, "days_push": 769, "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 5938, forks 1204 (observed 2026-08-28T04:09:32.550903+00:00)

## What it is
A collection of PyTorch implementations of deep reinforcement learning algorithms (DQN, PPO, SAC, TD3, DDPG, A3C, HER, hierarchical RL) with accompanying environments. It serves primarily as an educational reference for studying and comparing RL algorithm implementations.

## Use cases
- learn how DQN and its variants are implemented in PyTorch
- study reference implementations of PPO and SAC
- compare different deep RL algorithms side by side
- find a starting point for implementing a custom RL agent
- understand hindsight experience replay and hierarchical RL
- teach a reinforcement learning course with working code examples

## When to choose
- you want readable, well-documented implementations of classic deep RL algorithms
- you are learning reinforcement learning and want code alongside the papers
- you need a reference to check your own RL implementation against

## When to avoid
- you need a production-grade, high-performance RL training framework
- you want the latest algorithms with active development and community support
- you need distributed or large-scale RL training at scale

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, reinforcement-learning, deep-learning
- domain: reinforcement-learning, machine-learning, tutorials
- platform: python, cross-platform
- tags: pytorch, dqn, ppo, sac, td3, actor-critic, educational, reference-implementations

## Member repositories
- p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.550903+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 2026-08-29T17:50:46.776754+00:00, confidence not recorded.
  - readme: https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch (fetched 2026-08-28T04:09:32.550903+00:00, sha 1374becf23b7)
- Data as of 2026-08-30T08:39:29.467469+00:00.