Reinforcement Learning
Reinforcement Learning (Agent-Environment Reward Optimization) · Also known as: RL, reward-based learning, trial-and-error learning, policy optimization
Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.
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When to use it
Use reinforcement learning when the task is sequential decision-making with delayed rewards and no labeled training data: game playing, robotic control, resource scheduling, recommendation sequencing, or fine-tuning language models with human feedback (RLHF). RL is appropriate when the environment can be simulated cheaply and the agent can interact many times. Do NOT use RL when a supervised or self-supervised approach can solve the problem — RL is sample-inefficient and unstable; when the reward function is hard to specify; when interactions with the real environment are expensive or dangerous without a simulator; or when the dataset is static and fixed (use offline RL with caution).
Strengths & limitations
- Learns directly from interaction without labeled data, making it applicable where annotation is impossible.
- Capable of discovering superhuman strategies in complex sequential tasks (games, robotics, scheduling).
- Naturally models long-horizon planning and temporal credit assignment.
- Scales to high-dimensional state spaces (pixels, text) when combined with deep neural networks.
- RLHF enables aligning large language models with human preferences.
- Extremely sample-inefficient: may require millions of environment interactions to converge.
- Training is often unstable and sensitive to hyperparameters, reward shaping, and random seeds.
- Reward function design is difficult; poorly specified rewards cause unintended optimisation (reward hacking).
- Generalisation to unseen states or environments is not guaranteed.
- Real-world deployment is risky without extensive simulation; exploration can cause dangerous actions.
Frequently asked
What distinguishes reinforcement learning from supervised learning?
Supervised learning requires labeled input-output pairs and learns a fixed mapping. RL requires no labels; instead the agent receives reward signals after actions and must discover what to do through trial and error, making it suitable for sequential decision problems where the correct action is not known in advance.
What is the difference between model-free and model-based RL?
Model-free RL (e.g. DQN, PPO) learns policies or value functions directly from experience without an explicit model of environment dynamics. Model-based RL learns or is given a transition model and uses it for planning. Model-based methods tend to be more sample-efficient but require an accurate model, which can be hard to learn.
How do I choose between a value-based and policy-gradient method?
Value-based methods like DQN work well with discrete action spaces and are relatively stable. Policy-gradient methods (REINFORCE, PPO, SAC) handle continuous action spaces naturally and can learn stochastic policies. Actor-critic methods combine both. For most modern applications, PPO or SAC are safe starting points.
How many environment interactions does RL typically need?
RL is notoriously sample-hungry. Simple tabular tasks may converge in thousands of steps; Atari DQN required tens of millions of frames; complex robotics tasks can need hundreds of millions of simulation steps. This makes a fast simulator essential and rules out RL for many real-world problems with expensive interactions.
What is RLHF and why is it important?
Reinforcement Learning from Human Feedback trains a reward model from human preference comparisons and then fine-tunes a language model with RL to maximise that learned reward. It is the dominant technique for aligning large language models with human values and is central to models like InstructGPT and Claude.
Sources
- Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
- Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. DOI: 10.1038/nature14236 ↗
How to cite this page
ScholarGate. (2026, June 3). Reinforcement Learning (Agent-Environment Reward Optimization). ScholarGate. https://scholargate.app/en/deep-learning/reinforcement-learning
Which method?
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