Self-Learning systems with reward-driven behavior in complex environments
We build deep Reinforcement Learning algorithms that provide a framework for machine-based autonomous decision making by using probabilistic models. Our reinforcement learning algorithms aim at bringing out a positive reward or ideal behavior based upon the positive feedback/action from the environment. We follow the simple reward-and-punishment approach to train our algorithms and transform businesses.
Our AI-driven algorithms adapt to the environment over time, when needed, enabling autonomous and data-efficient decision making, and maximizing the reward in the long-term.
Reinforcement Learning is a machine learning technique that trains an algorithm following the cut-and-try approach. The algorithm (agent) evaluates a present situation (state), takes an action, and receives feedback (reward) from the environment.
Positive feedback is a reward and negative feedback is punishment for making a mistake.
We help businesses leverage the latest technologies in implementing reinforcement learning and help in taking sequential decisions and automate the decision-making process. Following are the use cases and applications where our learning solutions can be leveraged :
Our reinforcement learning capabilities help our industrial clients in reducing energy consumption in their data centers. Our clients can reduce downtime, improve energy efficiency, increase equipment longevity, and control vehicles and robots in real-time.
Our solutions automate and build intelligence into complex and dynamic systems in energy, HVAC, manufacturing, automotive and supply chains.
We provide robotics a framework and set of tools for hard-to-engineer behaviors. This could help in the exponential growth of robotics as reinforcement learning doesn’t need any supervision. Our learning algorithms are developed by highly skilled experts and are highly accurate and require less training data.
We develop models that save time and energy on designing the algorithms for the present problems and obtain solutions for the harder.
Our reinforcement learning solutions ensure optimal treatments for health conditions and drug therapies. They can also be used in clinical trials as well as for other applications in healthcare like dosage of medicines, optimization of treatment policies for those suffering from chronic ailments, etc.
Our RL solutions can be leveraged for the usage of medical equipment, dosage of medicines, and two-stage clinical trials.
A lot of our clients’ abstract texts (customer inquiries, chatbots, contracts, etc) are produced as highly readable text summaries with our reinforcement learning and advanced contextual text generation models, providing better text mining solutions to our clients.
Instead of relying on decision trees, our chatbots are RL trained and learn from user interactions.
The Financial sector is leveraging RL models to train their algorithms in enhancing trading and equity and automate the trading processes. Our RL models take into account the outcome of one’s actions on the market.
Our RL models will help our clients in optimizing their portfolios and provide for optimal trade execution.
Our reinforcement learning algorithms create a celestial customer experience and personalize customer interactions based on their preferences, background and online behavioral patterns.
Our RL models will enable our clients in optimizing their warehouse space, dynamic pricing, customer delivery, personalized recommendations, etc, thereby adding value to their businesses derived from higher customer satisfaction.
Personalizing the web services increase users’ satisfaction with a website or increase the yield of a marketing campaign. A simple type of reinforcement learning is the A/B testing, which is used to decide which of the 2 versions of the website, A or B, do the users prefer. With the appropriate website content, the features can be personalized.
Here the physical measurements of the surroundings are made by sensors on the robot. The rewards are given for completing a goal and can be adjusted for smoothness, economic use of energy, etc. The robot will be capable to decide on doing one of the many tasks as well as to control the motors to move to a different location.
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