What is reinforcement learning?Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error. Show
How does reinforcement learning work?In reinforcement learning, developers devise a method of rewarding desired behaviors and punishing negative behaviors. This method assigns positive values to the desired actions to encourage the agent and negative values to undesired behaviors. This programs the agent to seek long-term and maximum overall reward to achieve an optimal solution. These long-term goals help prevent the agent from stalling on lesser goals. With time, the agent learns to avoid the negative and seek the positive. This learning method has been adopted in artificial intelligence (AI) as a way of directing unsupervised machine learning through rewards and penalties. Applications and examples of reinforcement learningWhile reinforcement learning has been a topic of much interest in the field of AI, its widespread, real-world adoption and application remain limited. Noting this, however, research papers abound on theoretical applications, and there have been some successful use cases. Current use cases include, but are not limited to, the following:
Gaming is likely the most common usage field for reinforcement learning. It is capable of achieving superhuman performance in numerous games. A common example involves the game Pac-Man. A learning algorithm playing Pac-Man might have the ability to move in one of four possible directions, barring obstruction. From pixel data, an agent might be given a numeric reward for the result of a unit of travel: 0 for empty space, 1 for pellets, 2 for fruit, 3 for power pellets, 4 for ghost post-power pellets, 5 for collecting all pellets and completing a level, and a 5-point deduction for collision with a ghost. The agent starts from randomized play and moves to more sophisticated play, learning the goal of getting all pellets to complete the level. Given time, an agent might even learn tactics like conserving power pellets until needed for self-defense. Reinforcement learning can operate in a situation as long as a clear reward can be applied. In enterprise resource management (ERM), reinforcement learning algorithms can allocate limited resources to different tasks as long as there is an overall goal it is trying to achieve. A goal in this circumstance would be to save time or conserve resources. In robotics, reinforcement learning has found its way into limited tests. This type of machine learning can provide robots with the ability to learn tasks a human teacher cannot demonstrate, to adapt a learned skill to a new task or to achieve optimization despite a lack of analytic formulation available. Reinforcement learning is also used in operations research, information theory, game theory, control theory, simulation-based optimization, multiagent systems, swarm intelligence, statistics and genetic algorithms. Challenges of applying reinforcement learningReinforcement learning, while high in potential, can be difficult to deploy and remains limited in its application. One of the barriers for deployment of this type of machine learning is its reliance on exploration of the environment. For example, if you were to deploy a robot that was reliant on reinforcement learning to navigate a complex physical environment, it will seek new states and take different actions as it moves. It is difficult to consistently take the best actions in a real-world environment, however, because of how frequently the environment changes. The time required to ensure the learning is done properly through this method can limit its usefulness and be intensive on computing resources. As the training environment grows more complex, so too do demands on time and compute resources. Supervised learning can deliver faster, more efficient results than reinforcement learning to companies if the proper amount of data is available, as it can be employed with fewer resources. Common reinforcement learning algorithmsRather than referring to a specific algorithm, the field of reinforcement learning is made up of several algorithms that take somewhat different approaches. The differences are mainly due to their strategies for exploring their environments.
How is reinforcement learning different from supervised and unsupervised learning?Reinforcement learning is considered its own branch of machine learning, though it does have some similarities to other types of machine learning, which break down into the following four domains:
Readers looking for more information on deep learning and machine learning can follow these links to articles featuring in-depth breakdowns of those topics. This was last updated in March 2021 Continue Reading About reinforcement learning
Dig Deeper on AI technologies
What is a policy in reinforcement learning?A reinforcement learning policy is a mapping from the current environment observation to a probability distribution of the actions to be taken. During training, the agent tunes the parameters of its policy approximator to maximize the long-term reward.
What is an action in reinforcement learning?action. #rl. In reinforcement learning, the mechanism by which the agent transitions between states of the environment. The agent chooses the action by using a policy.
How do you define states in reinforcement learning?There are three basic concepts in reinforcement learning: state, action, and reward. The state describes the current situation. For a robot that is learning to walk, the state is the position of its two legs. For a Go program, the state is the positions of all the pieces on the board.
What is value and policy in reinforcement learning?For this purpose there are two concepts in Reinforcement Learning, each answering one of the questions. The value function covers the part of evaluating the current situation of the agent in the environment and the policy, which describes the decision-making process of the agent.
|