Standard 2 stops to get here · leads to 1
Exploration vs Exploitation
The RL dilemma of trying new actions (exploration) versus using known good actions (exploitation) to maximize reward.
Your route here
2 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Reinforcement Learning ✓ understood
Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.
- Exploration vs Exploitation · you are here ✓ understood
Where it sits
Explore nearby
Agents & RL Q-Learning A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state. Agents & RL Monte Carlo Tree Search A search algorithm combining tree search with random sampling, used in game-playing AIs. Agents & RL Policy A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning. Agents & RL Reward A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents. Training Online Learning Models that learn continuously from streaming data, updating incrementally as new data arrives.
In the research
All papers →A paper that builds on Exploration vs Exploitation .
