The idea that we learn by interacting with our environment is the first to occur to us when we think about the nature of learning. When an infant plays, waves its arms, or looks about, it has no explicit teacher, but it does have a direct sensory motor connection to its environment. Exercising this connection produces a wealth of information about cause and effect, about the consequences of actions, and about what to do in order to achieve goals. Throughout our lives, such interactions are undoubtedly a major source of knowledge about our environment and ourselves. Whether we are learning to drive a car or to hold a conversation, we are acutely aware of how our environment responds to what we do, and we seek to influence what happens through our behavior. Learning from interaction is a foundational idea underlying nearly all theories of learning and intelligence.
Reinforcement Learning is about observing in the environment, learning from it and act accordingly. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them. Learner learns from experience with the consequences of a chosen action and then updates beliefs about the value of the action and thereby improve future decisions - predict consequences of and optimize their behavior in environments in which actions lead them from one state or situation to the next and lead to immediate or subsequent rewards and punishments.
Reinforcement Learning is the branch of Artificial Intelligence that concerns how an agent, such as a robot, can learn by trial and error to make decisions to obtain better rewards and avoid punishments. Reinforcement learning is part of a decades long trend within artificial intelligence and machine learning toward greater integration with statistics, optimization, and other mathematical subjects.
Reinforcement learning is different from supervised learning and unsupervised learning. Supervised learning is learning from a training set of labeled examples provided by a knowledgeable external supervisor. Unsupervised learning is typically about finding structure hidden in collections of unlabeled data. Although Reinforcement Learning looks like unsupervised learning because it does not rely on examples of correct behavior, but it’s not. Reinforcement learning is trying to maximize a reward signal instead of trying to find hidden structure. Reinforcement Learning is the third Machine Learning paradigm, alongside supervised and unsupervised learning. A Reinforcement Learning agent includes followings; State (environment), Actions (policy), Reward (feedback)
Reinforcement Learning has become one of the most active research areas in machine learning, artificial intelligence, and neural network research. The field has developed strong mathematical foundations and impressive applications. The computational study of reinforcement learning is now a large field, with hundreds of active researchers around the world in diverse disciplines such as psychology, control theory, artificial intelligence, and neuroscience. Contributions establishing & developing relationships to the theory of optimal control and dynamic programming particularly have been important.
Our Deep Reinforcement Learning Training Course in Pune covers all latest techniques to create artificially intelligent Reinforcement Learning Agents that can solve varied complex tasks, with applications ranging from gaming to finance to robotics.
We are the pioneer in Artificial Intelligence training in Pune, imparting classroom training since the year 2014. Our senior Data Scientists from the industry with extensive experience on implementation of diverse types of Artificial Intelligence Projects have carefully designed this Deep Reinforcement Learning Course curriculum keeping in mind the latest advancements in this area.
Major topics covered in our Deep Reinforcement Learning Training Course in Pune are following:
Policy, Value, Action: All Reinforcement Learning algorithms involve estimating value functions - functions of states (or of state-action pairs) that estimate how good it is for the agent to be in a given state (or how good it is to perform a given action in a given state). The notion of "how good" here is defined in terms of future rewards that can be expected. Rewards, the agent can expect to receive in the future depend on what actions it will take. Accordingly, value functions are defined with respect to particular ways of acting, called policies. A policy is mapping from states to probabilities of selecting each possible action.
Learn Pathfinding techniques (for finding the shortest route between two points) using A* (star) Algorithm.
Learn the Action Value Function using Q-learning algorithm, a value-based Reinforcement Learning algorithm.
Markov Models: Reinforcement learning uses the formal framework of Markov Decision Processes (MDP) to define the interaction between a learning agent and its environment in terms of states, actions, and rewards. This framework is intended to be a straightforward way of representing essential features of the artificial intelligence problem. These features include a sense of cause and effect, a sense of uncertainty and nondeterminism, and the existence of explicit goals.
There are three fundamental classes of methods for solving finite Markov decision problems: Dynamic Programming, Monte Carlo methods and Temporal Difference Learning. Each class of methods has its strengths and weaknesses.
Dynamic Programming methods are well developed mathematically but require a complete and accurate model of the environment. Learn to write and implement iterative policy evaluation, policy improvement, policy iteration, and value iteration.
Monte Carlo methods are ways of solving the reinforcement learning problem based on averaging sample returns. Monte Carlo methods require only experience - sample sequences of states, actions, and rewards from actual or simulated interaction with an environment. Learning from actual experience is striking because it requires no prior knowledge of the environment's dynamics yet can still attain optimal behavior. Learning from simulated experience is also powerful. Learn to implement Monte Carlo prediction and control methods.
Temporal Difference methods require no model and are fully incremental but are more complex to analyze. The methods also differ in several ways with respect to their efficiency and speed of convergence.
Reinforcement Learning methods are broadly divided into two classes, model-based and model-free.
Model-based Reinforcement Learning uses experience to construct an internal model of the transitions and immediate outcomes in the environment. Appropriate actions are then chosen by planning or searching in this world model. This is a statistically efficient way to use experience, as each piece of information from the environment can be stored in a statistically faithful and computationally manipulable way.
Model-free Reinforcement Learning, on the other hand, uses experience to learn directly one or both of two simpler quantities (state, action, values or policies), which can achieve the same optimal behavior but without estimation or use of a world model.
Those who want to advance their skills in the area of advanced Artificial Intelligence, having significant knowledge of Machine Learning, Deep Learning Architectures, Neural Networks, familiarity with linear algebra, calculus, understand the differential mathematics, basics of probability and statistics, as well as decent Python coding skills preferably in data science.
If you’d like to learn Deep Learning, Neural Networks, consider our Deep Learning course in Pune. If you are a novice in Machine Learning & Python for Data Science, take up our Data Science Professional course in Pune. Also browse for our other Artificial Intelligence Training courses in Pune to pick a matching course for your AIML training need.
Earn a certification of completion at completion of our Deep Reinforcement Learning course in Pune.