Reinforcement learning (RL) is a machine learning technique where an agent learns to make optimal decisions in an environment through trial and error to maximize cumulative rewards. An agent interacts with an environment, taking actions, and receiving rewards or penalties based on those actions. Unlike other ML methods, RL doesn't have an "answer key"; instead, it learns a strategy, called a policy, to choose actions that lead to the best long-term outcomes.
Seeking canonical benchmark for this task.
Suggest one →Leading models across all datasets in this task.
No results yet. Be the first to contribute.
Didn't find the model, metric, or dataset you needed? Tell us in one line. We read every message and reply within 48 hours.
0 datasets tracked for this task.
No datasets tracked yet.
Still looking for something on Reinforcement Learning? A missing model, a stale score, a benchmark we should cover — drop it here and we'll handle it.
Real humans read every message. We track what people are asking for and prioritize accordingly.