CSKrishna/Optimal-bidding-policy-using-Policy-Gradient-in-a-Multi-agent-Contextual-Bandit-setting
We use policy gradient to help agents learn optimal policies in a competitive multi-agent contextual bandit setting
No commits in the last 6 months.
Stars
12
Forks
5
Language
Jupyter Notebook
License
—
Category
Last pushed
Mar 09, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/agents/CSKrishna/Optimal-bidding-policy-using-Policy-Gradient-in-a-Multi-agent-Contextual-Bandit-setting"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
facebookresearch/BenchMARL
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL...
datamllab/rlcard
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
Toni-SM/skrl
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with...
utiasDSL/gym-pybullet-drones
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
koulanurag/ma-gym
A collection of multi agent environments based on OpenAI gym.