sfujim/TD3

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2106 stars · 481 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The author's official PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3), a reinforcement learning algorithm for continuous control. It includes experiment scripts, a DDPG baseline for comparison, and the learning curve data from the ICML 2018 paper.

Use cases

• reproduce TD3 paper results on MuJoCo continuous control tasks
• train an RL agent on OpenAI gym environments like HalfCheetah
• compare TD3 against DDPG with matched hyperparameters
• use TD3 as a baseline for new continuous control RL research
• get the original learning curve data from the TD3 paper

When to choose

• you need the canonical, author-verified TD3 implementation for research or baselines
• you want to reproduce the ICML 2018 paper results on MuJoCo tasks
• you need a simple, minimal PyTorch RL codebase to modify or extend

When to avoid

• you need a maintained library with an API, docs, or support for new gym versions
• you want multi-algorithm RL frameworks like Stable-Baselines3 or RLlib
• you need discrete-action or offline RL support

Facets

library · maturity maintenance

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning python td3 actor-critic pytorch mujoco openai-gym continuous-control ddpg research-code research linux macos

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