Methods › Reinforcement Learning › Behaviour Policies › Epsilon Greedy Exploration

Epsilon Greedy Exploration

5 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ϵ-Greedy Exploration is an exploration strategy in reinforcement learning that takes an exploratory action with probability ϵ and a greedy action with probability 1-ϵ. 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 π in several state-of-the-art reinforcement learning models.

Image Credit: Robin van Embden

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response
4 Aug 2024 · 0 repositories · arXiv:2408.01999
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
20 Jun 2022 · 1 repository · arXiv:2206.09627
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning
26 Jan 2022 · 0 repositories · arXiv:2201.10803
Individual specialization in multi-task environments with multiagent reinforcement learners
29 Dec 2019 · 0 repositories · arXiv:1912.12671
Playing Atari with Deep Reinforcement Learning
19 Dec 2013 · 112 repositories · arXiv:1312.5602

Syntology ran 56 of 117 samples · 61 unverified · 56 pointer-only (licence)

Tasks archive 2025-07-28

14 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Reinforcement Learning (RL)	5
Q-Learning	3
Reinforcement Learning	3
reinforcement-learning	3
Atari Games	2
Deep Reinforcement Learning	2
Multi-agent Reinforcement Learning	2
Decision Making	1
Fairness	1
Malware Analysis	1
Multi-Goal Reinforcement Learning	1
SMAC	1
SMAC+	1
Starcraft	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Behaviour Policies

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections