Reinforcement learning, robotics and current research
AI research explores learning agents, environment models and combinations of rules and learning.
What you will learn
- Explain: learning from actions and rewards
- Apply the idea in an example: Reinforcement learning, robotics and current research
- Recognize limitations and verify the exercise outcome
AI research explores learning agents, environment models and combinations of rules and learning. Separate demonstrations from hypotheses.
This is a conceptual or external lab. It does not assume the application exposes every control described.
How it works, step by step
Learning from actions and rewards
In reinforcement learning an agent observes an environment, selects actions and receives rewards. A policy describes action selection; the objective is cumulative performance rather than immediate reward alone. A poorly designed reward can encourage unwanted behavior. Robots add sensors, actuators and physical constraints. A world model tries to model how the environment changes, but predictions need validation. Human feedback can help adapt behavior without guaranteeing safety. An LLM agent using tools is not automatically being trained online with RL.
How learning changes a model
Training data is the set of examples used to adjust model parameters. Supervised learning includes a target, such as “spam”. Unsupervised learning finds structure, such as customer groups. Self-supervised learning creates targets from data, for example predicting following text. Validation helps choose settings; the test set stays separate until final evaluation. A model that memorizes examples and fails on new data is overfitting. A proper split prevents information from the test set leaking into training.
Define success before optimizing
Prepare representative requests and expected answers or properties. Include ordinary cases, ambiguity, missing data and tool errors. For RAG, measure evidence retrieval separately from answer correctness. Recall@k measures the share of relevant evidence found in the first k results; precision@k measures the share of returned results that are relevant. For agents, also track actions, permissions and stopping. An LLM-as-judge can speed evaluation but needs calibration against humans. Do not change prompt, model and index simultaneously if you want to understand what improved results.
The visual map
Follow the solid arrows for the main flow. Dashed blue arrows supply data or context; dashed pink arrows show feedback or returning results. Colors and shapes distinguish models, stores, decisions and outputs. Reinforcement learning and robotics are external topics; reward-driven training is distinct from ordinary agent inference. On smaller screens, scroll horizontally to follow the entire diagram.
A complete example
In a maze, rewarding the exit differs from rewarding movement. A world model may predict transitions, while symbolic rules forbid zones. Robotics adds physical limits.
Try it yourself
- Design maze rewards and identify one way to exploit them.
- Record the input, source and expected outcome before running the experiment. Use only the fictional data in the example.
- Follow the diagram stages. At every step record what information is received and produced; do not confuse intermediate output with the final outcome.
- Repeat after removing necessary information or making the input ambiguous. Check whether the system clarifies, stops or invents an answer.
- Compare with the explained solution. Keep the configuration, date, result and an explanation for differences. Change one thing and retest.
An explained solution
The policy follows the designed objective and is tested in new situations; benchmark performance does not establish AGI. A successful exercise lets you show the connection between input, stages and outcome. When information is missing, a cautious answer is more useful than invented details. Compare more than style: check conditions, sources and operations too.
When it helps and what can go wrong
Neuro-symbolic systems, world models and AGI are not synonyms. Choose this approach when it improves a measured need. Keep a simple baseline and compare outcomes using identical inputs. One successful example does not establish reliability in every situation.
Check your understanding
Does every tool-using agent learn with RL during a conversation?
No. Using a trained model and retraining are distinct processes.
Why not evaluate only on training data?
We want performance on new situations, not proof of memorization.
What outcome should this exercise produce?
The policy follows the designed objective and is tested in new situations; benchmark performance does not establish AGI.
Words to remember
- Policy: A learned rule for selecting actions.
- Overfitting: The model fits the examples it has seen too closely.
- Regresie / Regression: A change that breaks a previously correct case.
Sources and your next step
To prepare: X04 — Adapting and serving models