Definition
Reinforcement Learning (RL) is the science of decision-making. It is about learning the optimal behavior in an environment to obtain maximum reward. This optimal behavior is learned through interactions with the environment and observations of how it responds, similar to children exploring the world around them and learning the actions that help them achieve a goal.
How Does Reinforcement Learning Work?
The Reinforcement Learning problem involves an agent exploring an unknown environment to achieve a goal. RL is based on the hypothesis that all goals can be described by the maximization of expected cumulative reward. The agent must learn to sense and perturb the state of the environment, using its actions to derive maximal reward. The formal framework for RL borrows from the problem of optimal control of Markov Decision Processes (MDP).
The main elements of an RL system are:
• The agent or the learner
• The environment the agent interacts with
• The policy that the agent follows to take actions
• The reward signal that the agent observes upon taking actions
Examples of Reinforcement Learning
Alpha Go. One of the most complex strategic games is a 3,000-year-old Chinese board game called Go. Its complexity stems from the fact that there are 10^270 possible board combinations, several orders of magnitude more than the game of chess. In 2016, an RL-based Go agent called Alpha Go defeated the greatest human Go player. Much like a human player, it learned by experience, playing thousands of games with professional players. The latest RL-based Go agent has the capability to learn by playing against itself, an advantage that the human player doesn’t have.
Autonomous Driving. An autonomous driving system must perform multiple perception and planning tasks in an uncertain environment. Some specific tasks where RL finds application include vehicle path planning and motion prediction. Vehicle path planning requires several low and high-level policies to make decisions over varying temporal and spatial scales. Motion prediction is the task of predicting the movement of pedestrians and other vehicles, to understand how the situation might develop based on the current state of the environment.
Benefits of Reinforcement Learning
Focuses on the problem as a whole. Conventional machine learning algorithms are designed to excel at specific subtasks, without a notion of the big picture. RL, on the other hand, doesn’t divide the problem into ; it directly works to maximize the long-term reward. It has an obvious purpose, understands the goal, and is capable of trading off short-term rewards for long-term benefits.
Does not need a separate data collection step. In RL, training data is obtained via the direct interaction of the agent with the environment. Training data is the learning agent’s experience, not a separate collection of data that has to be fed to the algorithm. This significantly reduces the burden on the supervisor in charge of the training process.
Works in dynamic, uncertain environments. RL algorithms are inherently adaptive and built to respond to changes in the environment. In RL, time matters and the experience that the agent collects is not independently and identically distributed (i.i.d.), unlike conventional machine learning algorithms. Since the dimension of time is deeply buried in the mechanics of RL, the learning is inherently adaptive.
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