KornbergFresnel/ModelRepo
reproduce some RL or Multi-Agent models
This project helps machine learning researchers and practitioners reproduce and experiment with established deep reinforcement learning (RL) and multi-agent RL models. It takes in specified deep RL algorithms and multi-agent environments (like 'particle' environments) and outputs trained models that can be analyzed and extended. The primary user is someone working on advanced AI research or applications involving autonomous agents.
No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer looking to implement, replicate, or benchmark advanced deep reinforcement learning or multi-agent reinforcement learning algorithms.
Not ideal if you are looking for a simple, out-of-the-box solution for basic single-agent RL tasks or if you are new to deep reinforcement learning concepts.
Stars
35
Forks
10
Language
Python
License
—
Category
Last pushed
May 22, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/agents/KornbergFresnel/ModelRepo"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Toni-SM/skrl
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with...
facebookresearch/BenchMARL
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL...
utiasDSL/gym-pybullet-drones
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
datamllab/rlcard
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
proroklab/VectorizedMultiAgentSimulator
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement...