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Research Proposal on Reinforcement Learning

Research Proposal on Reinforcement Learning

   Reinforcement learning is one of the learning paradigms in machine learning, whereas a learning agent interacts with the environment and, perceiving the consequences of its actions, can learn to change over its behavior concerning rewards acquired. Reinforcement learning develops artificial intelligent systems accentuating real-time feedback.
   By leveraging the procured rewards, the learning agent seeks to enhance their knowledge about the environment for determining future action. The principal target of reinforcement learning is to decide the best sequence of action for each given state in the environment for maximizing the cumulative reward. Reinforcement learning is an active research area, and its progression has been significantly high recently.

Impressive Applications of Reinforcement Learning: With abundant, successful applications in gaming, plant control, and business intelligence, the reinforcement learning technique is considered ideal for decision-making in obscure models or an unknown environment. It is also suitable for real-time applications such as personalized web services, finance sector, PC games, inventory management, robotics in industrial automation, traffic light control, bidding, advertising, and many more. Some of its impressive applications in Reinforcement learning are highlighted here;
Self-driving cars: Deep reinforcement learning paves the way for autonomous driving, more effectively in self-driving cars. Some reinforcement learning-based autonomous driving tasks include trajectory optimization, motion planning, dynamic pathing, and controller optimization.
Trading and finance: Reinforcement learning helps in the trading and stock market environment by interacting with it. Reinforcement learning effectively predicts the stock prices and financial trades.
Natural Language Processing (NLP): Reinforcement learning enables various NLP tasks combined with deep learning algorithms. Some of its popular NLP applicative tasks are policy learning, dialogue generation, machine translation, abstractive text summarization, and question answering.
Healthcare: In the healthcare sector, reinforcement learning applications are categorized as dynamic treatment regimes(DTRs) and automated medical diagnosis. DTRs diagnose chronic diseases such as cancer, diabetes, anemia, HIV, mental illness, and critical care such as sepsis and anesthesia. Other general domains where reinforcement learning is applied are drug discovery and health management.
Gaming: Reinforcement learning conducts random actions in the gaming and development domain. Gaming application is challenging for testing reinforcement learning techniques. It is also effectively applied for training in modern mobile games by learning to discredit action spaces under multiple environments.
Marketing and advertising: With multi-agent reinforcement learning, a distributed coordinated multi-agent bidding is developed in the area of advertising. In marketing, reinforcement learning facilitates predicting future customer actions, extensively advancing outcomes.
Robotics: The integration of reinforcement learning and deep learning can train robots for robot manipulating and controlling tasks. Reinforcement learning reaches high scope in designing intelligent robots with humanoid perception. Presently, reinforcement learning attains a series of significant discoveries in robotic applications because of its super-computational power, frontier algorithms, and large-scale dataset.

Research Challenges in Reinforcement Learning: Reinforcement learning is useful in various applications despite facing ridiculous challenging issues. Some of its challenges are listed below;
  •  Recently, in reinforcement learning, accomplishing multi-task learning is the major challenge, whereas the critical point of the issue is scalability.
  •  Another challenge in reinforcement learning is a safe and effective exploration.
  •  Moreover, the trade-off between exploration, exploitation, and long-term credit assignment remains a stumbling block.
  •  The computational power required by reinforcement learning hinders several techniques, including exhaustive search and tabular methods.
  •  One of the core challenges with reinforcement learning is sample efficiency with limited samples. Sample efficiency describes an algorithm making the most of the given sample, which is essential for efficient performance.
  •  Generalization is another constraint in reinforcement learning, which is conducive to increasing similarity and handling differences between training and testing and improves reinforcement-specific problems.

Potential Future Scope of Reinforcement Learning: Even though reinforcement learning conquers the challenges such as stability, convergence, and optimality analysis. Moreover, it entails the additional mechanism to improve the learning efficiency of the system. Notably, it is considered inappropriate to take the best actions for the real-time environment, whereas the dynamics in the environment often become a great deal for the agent to learn and circumvent the problem above. Some of its future developments are discussed here;
  •  For the enhancement of generalization, reinforcement learning incorporates fast online adaptation, tackling reinforcement learning specific issues, novel architectures, model-based reinforcement learning, environment generation, offline reinforcement learning, reward-function variation, context-efficiency, and continual reinforcement learning.
  •  Multi-agent reinforcement learning should be improved to handle issues like sparse rewards, sample efficiency, and multiple equilibriums.
  •  Hierarchical reinforcement learning is another future scope that needs more research, focusing on sparse rewards and long horizons, which are beneficial to transfer and multi-task learning.

Notable Highpoints of Reinforcement Learning:
  •  Reinforcement learning (RL) imparts a rich speculative framework for understanding human learning and decision-making.
  •  Lately, reinforcement learning has enabled stunning advances in behavioral and neuro-scientific research by offering diverse applicative ideas.
  •  Reinforcement learning emerged as a state-of-the-art deep learning model, and synchronously, its promising ability to interact experience with the world and evaluative feedback.
  •  RL confronts sequential decision-making problems with sampled, analytic and deferred feedback simultaneously.
  •  Distinctive features of the reinforcement learning technique are suitable for implementing powerful solutions to various application problems.
  •  RL techniques significantly focus on handling varying environments to help autonomous agents modify to varying operating conditions.
  •  Recent progress in reinforcement learning, established by combining deep learning paradigms, has given rise to breakthroughs in many artificial intelligence tasks and devised deep reinforcement learning as a field of research.
  •  Multi-agent reinforcement learning is another promising research area, focusing on multiple agents interacting in a shared environment and producing complex group dynamics as the outcome.
  •  In the future scope, reinforcement learning will be integrated with many learning concepts to make problem-solving more autonomous and real-world complications than ever before.