What is learning through reinforcement called?

In recent years, significant progress has been made in the area of deep reinforcement learning. Deep reinforcement learning uses deep neural networks to model the value function (value-based) or the agent’s policy (policy-based) or both (actor-critic). Prior to the widespread success of deep neural networks, complex features had to be engineered to train an RL algorithm. This meant reduced learning capacity, limiting the scope of RL to simple environments. With deep learning, models can be built using millions of trainable weights, freeing the user from tedious feature engineering. Relevant features are generated automatically during the training process, allowing the agent to learn optimal policies in complex environments.

Traditionally, RL is applied to one task at a time. Each task is learned by a separate RL agent, and these agents do not share knowledge. This makes learning complex behaviors, such as driving a car, inefficient and slow. Problems that share a common information source, have related underlying structure, and are interdependent can get a huge performance boost by allowing multiple agents to work together. Multiple agents can share the same representation of the system by training them simultaneously, allowing improvements in the performance of one agent to be leveraged by another. A3C (Asynchronous Advantage Actor-Critic) is an exciting development in this area, where related tasks are learned concurrently by multiple agents. This multi-task learning scenario is driving RL closer to AGI, where a meta-agent learns how to learn, making problem-solving more autonomous than ever before.

The strengthening of behavior which results from reinforcement is appropriately called ‘conditioning’. In operant conditioning we ‘strengthen’ an operant in the sense of making a response more probable or, in actual fact, more frequent. — B. F. Skinner

Learning is a change in behavior or in potential behavior that occurs as a result of experience.  Learning occurs most rapidly on a schedule of continuous reinforcement.  However it is fairly easy to extinguish� switching to variable reinforcement after the desired behavior has been reached prevents extinction.

CLASSICAL CONDITIONING

If a neutral stimulus (a stimulus that at first elicits no response) is paired with a stimulus that already evokes a reflex response, then eventually the new stimulus will by itself evoke a similar response.  (UCS, UCR, CS, CR)

�        Each pairing of the CS with the UCS strengthens the connection between the CS and CR.

�        Timing is important.  Usually the strongest and fastest conditioning occurs when the CS is presented about � to one second before the UC.

�        EXTINCTION - If the CS is presented repeatedly in the absence of the UCS, the CS-CR bond will weaken and the CR will eventually disappear.

�        STIMULUS GENERALIZATION - Once conditioning has occurred the subject may respond not only to the CS, but to stimuli similar to it.  For example, many of our likes and dislikes of new people and situations come from generalization based on similarities to past experiences.

�        STIMULUS DISCRIMINATION � opposite of stimulus generalization.  SD is the ability to detect differences among stimuli.  This procedure is sometimes used to test the ability of nonverbal subjects to discriminate among various stimuli, such as color (air puff / eye blink).

OPERANT CONDITIONING

The organism operates on its environment in some way; the behavior in which it engages are instrumental to achieving some outcome.

LAW of EFFECT

If a response is followed by a pleasant or satisfying consequence, that response will be strengthened.  If a response is followed by an unpleasant or negative state of affairs, it will be weakened.

Differences Between Operant and Classical Conditioning

1)      In classical conditioning, the conditional behavior (CR) is triggered by the particular stimulus (CS) and is therefore called an elicited behavior.  Operant behavior is an emitted behavior in the sense that it occurs in a situation containing many stimuli and seems to be initiated by the organism.  In a sense the subject chooses when and how to respond.

2)      In classical conditioning, behavior (CR) is affected by something that occurs before the behavior (the CS-UCS pairing).  In contrast, the operant response is affected by what happens after the behavior � that is by its consequences.

Positive Reinforcement

Any stimulus or event that increases the likelihood of the occurrence of a behavior that it follows.

Shaping

Shaping is the method of successive approximations.  Shaping reinforces the behaviors as they get closer and closer to the desired behavior.

Negative Reinforcement

Negative Reinforcement is anything that increases a behavior that results in the reinforcers removal.

What is learning through reinforcement?

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.

What is another name for reinforcement theory?

This theory, in management, can also be referred to as operant conditioning or the law of effect. Quite simply, this theory notes that a behavior will continue with a certain level of frequency based on pleasant or unpleasant results.

What are 4 types of reinforcement theory?

There are four types of reinforcement: positive reinforcement, negative reinforcement, extinction, and punishment.

Why is it called reinforcement learning?

The “reinforcement” in reinforcement learning refers to how certain behaviors are encouraged, and others discouraged. Behaviors are reinforced through rewards which are gained through experiences with the environment.