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Reinforcement Learning (RL) is a type of machine learning that lets an agent interacts with the world and periodically learn in environment by making mistakes and receives rewards. In chess, for instance, you get 1 reward for a victory, 0 for a loss, and ½ for draw. Almost all RL issues may be stated using Markov Decision Processes (MDPs), mathematical frameworks used to represent an environment in RL. A finite environment S, a set of actions A(s) in that environment, a reward function R(s) with real values, and a transition model are the components of a Markov decision process. There are hundreds of different reinforcements learning algorithms available today, it divides the methods into two categories: model-based and model-free reinforcement learning.
In model-based RL, an environment transition model is used by the agent to analyze reward signals and decide what to do. If the model is unknown, then agent will have to learn the model by observing the outcomes of its actions and learn a useful utility function U(s). While in model free RL, agent has no preexisting knowledge or ability to acquire a model of environmental transitions. It learns a more simplified representation of appropriate behavior using two approaches, action utility learning and policy search. The most popular type of action utility learning is done by a Q-learning agent, which learns an action-utility function (Q-function) that indicates the predicted utility of performing a given action in a given condition. In policy search, a reflex agent learns a policy that directly links states to actions.
Learning the benefits of the states when the agent’s policy is fixed is the task of passive learning. The agent policy is set so that it always performs the action π(s), in state s. The objective is to understand the utility function Uπ (s), and how effective the policy is. Both the transition model P(s‘|s,a), and the reward function are unknown to the agent. In direct utility estimate, the reward and the expected utility of the subsequent states (expected reward-to-go) determine a state’s utility, and the utility values follow the Bellman equations. Due to the fact that utilities of states are not independent, they must obey the Bellman equations for a specific policy and do a considerably wider search than necessary. As a result, the method frequently converges very slowly. However, an adaptive dynamic programming (ADP) agent learns the transition model between states, taking use of the constraints between their utility, and then uses dynamic programming to solve the related Markov decision process. A further method is to modify the utilities of the observed states to make them consistent with the constraint equations by using the observed transitions. In general, α is the learning rate parameter is applied to the following update presented in below formula is often called the temporal-difference (TD) equation.
Uπ (s) ← Uπ (s) + α [R(s) + ϒUπ (s‘) — Uπ (s)]
Where, ϒ mean discount factor, it doesn’t need to transition model and not learn fast as the ADP agent and shows higher variability. However, it is a lot more straightforward and needs far less work per observation. Additionally, passive learning agents have fixed policies that dictate their behavior, meaning they are told what to do. This is what inspired us to create active learning agents.
Active learning agent needs to decide what to do as there’s no fixed policy that it can act and learn an optimal policy. Optimal actions can be learned utilizing a passive ADP agent and iterative value or policy evaluation. However, this strategy produces a greedy agent. Consequently, we employ a method that assigns greater weight to undiscovered behaviors and less weight to acts with lower utility. However, the agent is unaware of the actual environment, hence it cannot estimate the optimal response for the actual environment. The agent must choose between exploiting the optimal action to, maximize its short term benefit and exploring undiscovered states, to gather knowledge that lead to a policy change. Thus far, we have considered that an agent is free to investigate as it pleases, and that any negative rewards serve merely to improve its world model. This approach is useful for gaming or simulation for a self-driving car, but the actual world is less accommodating as many actions are irreversible. We cannot allow our agents to act irrevocably or to exist in absorbing states. An agent practicing driving in a real car, for example, should refrain from engaging in actions that could result in states with significant negative rewards, such serious auto accidents.
Even though the policy is suboptimal for the maximum-likelihood model, it is desirable to adopt a policy that works well for all models with a fair probability of being the real model. Three mathematical methods share flavor. First, Bayesian reinforcement learning starts with a prior probability P(h) over hypotheses h about the correct model and uses Bayes’ rule to calculate the posterior probability P(h|e) based on observations. If the agent stops learning, the best policy has the highest expected utility. Second method, originating in suitable control theories, permits a collection of potential models H without assigning probabilities and defines an optimum robust policy as one that yields the best outcome in the worst scenario over H. Frequently, H set will be the set of models that above some likelihood threshold on posterior probability; hence, the Bayesian and robust techniques are connected.
Next to ADP for a temporal difference (TD) learning agent, the most obvious change is that the agent will need to learn a transition model in order to choose a course of action on U a one-step look-ahead. Update and convergence of the model acquisition problem are identical to ADP. Instead of learning a utility function U(s), the Q-learning no need for look ahead technique avoids the necessity for a model by teaching learners an action-utility function Q(s). With knowledge of the Q-function, the agent can make the best possible decisions by argmaxa Q(s, a). Here Q(s, a) mean the expected reward by action a in state s. The following is the equation for the TD update for utilities using Q-learning as an example.
Q(s,a) ← Q(s,a) + α(R(s) + ϒmaxa‘ Q(s‘,a‘) — Q(s,a))
What is Q-learning ?
Q-learning is an off-policy learning algorithm that picks the optimum course of action by learning Q-value. In contrast, if we take SARSA (for state, action, reward, state, action) its relatively similar as Q-learning but its on-policy and backs up the Q-value for an action by waiting until it is taken before doing so. Both learn the best course of action in a 4×3 world, but much more slowly than the ADP agent.
For real world environments they convergence will be slow so, an evaluation function needs to be introduced in term of function approximation. It refers to the process of generating a approximation of the genuine utility function or Q-function as function approximation. For instance, the utility function may be approximated utilizing a linear weighted combination of characteristics or features f1, f2, …., fn. We lack linear functions that come close to approaching the utility function and may be unable to invent the essential features in new domain. Due the fact the researcher introduced deep reinforcement learning which use deep neural networks as function approximator. In equation below deep network is a function parameterized by ϴ, and all the weights in all the layers of network.
U’ϴ (x, y) = ϴ0 + ϴ1x + ϴ2y
While deep RL has achieved remarkable success, it still confronts serious challenges. Achieving decent performance is challenging, and a trained system may exhibit highly unpredictable behavior if the environment deviates even little from the training data. Deep Q-network (DQN) system was developed by DeepMind. To look for the application of DQN’s was trained independently on each 49 Atari video game. It was taught how to use paddles to bounce balls and how to drive a simulated race vehicle. Every time, the agent used the game score as a reward signal to train a Q-function from raw picture data. Although it struggled with a few games, overall, the system’s performance was close to that of a human expert. Another most prominent application was ALPHAGO which beat the top human player at the game GO. A value function and Q-function were learnt to direct the search by indicating which choices should be pursued further. Without any further search on the part of the player, the Q-function, is accurate enough to beat the vast majority of amateur human players.
A second strategy for dealing with lengthy sequences of action is hierarchical reinforcement learning (HRL), which aim is to divide them into smaller chunks, and then those chunks into even smaller ones, and so on, until the sequences are finally short enough to be easily memorized. Initially, a reinforcement learning agent with a hierarchical structure is programmed with a partial program that specifies the agent’s desired behavior. Simply giving the agent a simple partial program that allows it to choose any action from A(s), the set of activities that can be carried out in the current state s, will enough. The concept of the joint state space, in which each state is made up of a physical state and a machines state m, is the theoretical basis of HRL. Providing a natural additive breakdown of the total utility function, hierarchical RL can be a powerful tool for learning complicated actions.
Finally, simplest of all the methods is the policy search approach consider for reinforcement learning problems. As policy is a function π largely in multidimensional representations that have fewer parameters than there are states in the state space, which translates states to actions. In comparison to Q-Learning policy search look for a value of ϴ that results in good performance. The drawback is it make gradient-based search difficult as policy change discontinuously and often use a stochastic policy. Different strategies, such as starting with the simplest scenario — a deterministic policy and a deterministic environment — can be used to enhance this policy.
Challenges: Challenges may arise while using RL to address business issues. Since there is no labeled or unlabeled data to direct the agent, it must collect data as it goes. One’s choices affect the information one subsequently receives. Because of this, the agent may need to experiment with multiple approaches to get information. Environment unpredictability, training an RL algorithm in such isolated, simulated situations can boost its performance significantly. For instance, in video games, the agent’s decision-making context is static. Another possible challenge is delay feedback as in practical settings, it’s difficult to predict how long it will take for a decision to bear fruit. For instance, we may have to wait a month, a year, or even several years to see if an AI trading system’s prediction that investing in certain assets (real estate) will be beneficial was accurate.
Conclusion: Reinforcement learning is employed successfully in real-world business contexts despite the challenges it presents during training. RL is helpful when it’s necessary to get the best answers in a dynamic setting. Without a question, reinforcement learning is a state-of-the-art tool with enormous transformative potential. Nonetheless, it is not mandatory in all circumstances. In spite of this, idea of RL appears to be the most plausible technique to make a machine creative, as being open to novel approaches to completing a job is, by definition, creative. Therefore, reinforcement learning might be the next stage in artificial intelligence.
Refrence:
Artificial Intelligence: A Modern Approach, Global Edition Copertina flessibile — 20 maggio 2021