Reinforcement Learning

Reinforcement learning (RL) is a kind of machine learning concerned with how intelligent agents take decisions in a dynamic environment in which it is supposed to perform a certain goal, so that the cumulative reward is maximized. The environment is the world that the agent lives in and interacts with. The agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. On repeat, the agent tries to minimize the wrong ones and maximize the right ones. It is one of the three basic categories of machine learning, alongside supervised learning and unsupervised learning.

Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience. The birth of reinforcement learning goes back all the way to 1957 when Richard Bellman derived the Bellman equation. It is associated with dynamic programming and used to calculate the values of a decision problem at a certain point by including the values of previous states. Also, the model-free algorithm, Q-learning is based on this equation.

There are two types of Reinforcement:

Positive Reinforcement is when a particular behavior is associated with an increase in the reward ,resulting in an increase in the strength and frequency of the behavior.

Negative Reinforcement is the strengthening of a behavior because a negative condition is stopped or avoided.

Apart from the agent and the environment, there are four main sub elements of a reinforcement learning system:

A policy is a mapping from perceived states of the environment to actions to be taken when in those states. A reward signal defines the goal in a reinforcement learning problem. On each time step, the environment sends a single number, a reward to the reinforcement learning agent. The agent’s sole objective is to maximize the total reward it receives over the long run. The reward signal thus defines what are the good and bad events for the agent. A value function specifies what is good in the long run. Models are used for planning, by which we mean any way of deciding on a course of action by considering possible future situations before they are actually experienced. Q-learning and SARSA (State-Action-Reward-State-Action) are two commonly used RL algorithms.

Reinforcement learning has applications in several fields including information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, game theory, control theory and operations research.

One example of reinforcement learning in action is a robot learning how to walk. The robot first takes a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to take a smaller step. Thus, the robot is able to move forward.

Applications of Reinforcement learning:

2. In gaming: Using reinforcement learning, AlphaGo Zero was able to learn the game of Go from scratch. Deep reinforcement learning algorithms are being tested on games like chess, GO, and Atari. Companies like DeepMind and OpenAI have conducted extensive research in this area and established gyms where reinforcement learning models may be trained.

Reinforcement Learning is undoubtedly one of the most innovative technologies to be discovered in this century. Stay tuned to read more such articles!

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