computer-vision-14
Reinforcement Learning (RL)
3260 papers • 126 benchmarks • 313 datasets
Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in reinforcement-learning-rl-28
Trend
Dataset
Best Model
Actions
ProcGen
ProcGen
Libraries
iUse these libraries to find reinforcement-learning-rl-28 models and implementations
28 papers 2,393
