Standard 3 stops to get here · leads to 2
Environment
In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.
Your route here
3 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.
- Agent ✓ understood
In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.
- Environment · you are here ✓ understood
Where it sits
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Agents & RL Reward A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents. Agents & RL Policy A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning. Agents & RL Markov Decision Process A mathematical framework for modeling sequential decision-making with states, actions, rewards, and transition probabilities. Agents & RL World Model A learned model of environment dynamics that can predict future states, used in model-based RL. Agents & RL Sim-to-Real Transfer Transferring policies trained in simulation to real-world deployment, crucial for robotics.