Behaviour Policies

Epsilon Greedy Exploration

$\epsilon$-Greedy Exploration is an exploration strategy in reinforcement learning that takes an exploratory action with probability $\epsilon$ and a greedy action with probability $1-\epsilon$. It tackles the exploration-exploitation tradeoff with reinforcement learning algorithms: the desire to explore the state space with the desire to seek an optimal policy. Despite its simplicity, it is still commonly used as an behaviour policy $\pi$ in several state-of-the-art reinforcement learning models.

Image Credit: Robin van Embden


Paper Code Results Date Stars


Task Papers Share
Reinforcement Learning (RL) 4 33.33%
Atari Games 2 16.67%
Multi-agent Reinforcement Learning 2 16.67%
SMAC 1 8.33%
SMAC+ 1 8.33%
Starcraft 1 8.33%
Fairness 1 8.33%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign