Generative AI
How is reinforcement learning shaping the future of AI?
Agents are deeply embedded in all artificial intelligence and robotics systems, making autonomous decisions, learning from datasets, and interacting with the environment and humans. As we move towards more autonomous systems in a creator economy, AI agents will use reinforcement learning to transform the way they make decisions, interact with the environment and humans, and arrive at results in a complex and dynamic environment.
Reinforcement learning in AI gives agents the ability to apply logic and intuition like a human while ensuring a fair and bias-free, transparent, secure, and robust environment with a continuous feedback and monitoring process.
This article discusses the concept of reinforcement learning, advancements, and real-life applications and compares it with machine learning.
Reinforcement learning from trial and error
Reinforcement learning (RL) trains the AI agents by making them interact with the environment and learn from its successes and failures. This is like the way humans learn from their environment, as they receive a reward (for success) and a penalty (for mistakes). Over time, AI agents learn the optimal actions to take in situations to maximise the reward.
For example, an autonomous vehicle AI agent learns by navigating through traffic. The algorithm rewards the agent for navigating, braking, and accelerating smoothly and penalises it for driving harshly or getting too close to other vehicles. Over time, the AI agent learns to make safe driving decisions.
Reinforcement learning vs. machine learning
Machine learning (ML) and reinforcement learning in AI are complementary forces. ML uses supervised learning techniques to train the AI model using labelled data where the input and output are known. It also uses unsupervised learning techniques where the algorithms detect and fetch patterns in the input without a set output for clustering and dimensionality reduction.
Thus, reinforcement learning becomes a subset of machine learning, offering complementary solutions for diverse challenges.
Advancements in reinforcement learning
Researchers in the field of AI are using reinforcement learning to handle newer challenges to enhance its capabilities. Some of the advancements include –
Transfer learning
This advancement reduces the time an AI model takes to learn by allowing the agents to apply the knowledge they acquire in one task to other related ones. It reduces the training time and computational resources required and makes the RL model scalable and efficient.
Deep reinforcement learning
Deep reinforcement learning (DRL) combines deep learning and neural networks with RL to handle complex state spaces. A classic example is AlphaGo, an AI model that mastered the ancient Chinese game of Go, which requires strategy, ingenuity, and creativity, and defeated the current world champion with a score of 5-0.
Inverse reinforcement learning
IRL learns from an agent’s reward by observing its behaviour. This AI technique imitates human behaviour for efficient and human-like decision-making in diverse applications.
Multi-agent systems
In a real-life scenario, multiple human beings interact and learn from each other from their successes and mistakes. The next stage of reinforcement learning, in which researchers are currently working in a multi-agent environment in which several AI agents interact and learn from each other. This may prove groundbreaking in applications such as traffic management and collaborative robotics.
Reinforcement learning applications and case studies
Reinforcement learning with human feedback (RLHF) is spanning industries with critical real-world applications such as –
Game AI and interactive agents
The gaming industry can use RLHF to train non-player characters (NPCs) to exhibit human-like behaviour, such as the one we saw in the example of AlphaGo. The agents use human feedback to learn and adapt strategies, thus enhancing the gaming experience.
Robotics and automation
Train robots to perform complex tasks using RLHF while being conscious of human preferences and safety. For example, based on human feedback, robots can learn to manipulate objects and navigate through complex environments while remaining within the safety protocols.
Conversational AI and language models
Use RLHF to train large language models (LLMs) for conversational AI assistants by incorporating human feedback to manage quality, appropriateness, and coherence. The models align with human preferences and communication styles.
Healthcare applications
RLHF incorporates feedback from medical professionals and patients for planning treatments and creating decision support systems. AI systems thus make informed decisions about treatments considering the patient’s needs and values.
Tutoring systems
Intelligent tutoring systems using RLFG learn from and adapt to the student’s learning style and preferences. It incorporates feedback from students and teachers and craft personalised learning experiences by managing the content, teaching methods, and pace for maximum engagement and understanding.
How can Infosys BPM help implement reinforcement learning in AI?
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