#ReinforcementLearning

[bharatpatel1061]

Beyond Scripts: How AI Agents Are Replacing Hardcoded Logic

Introduction: Hardcoded rules have long driven traditional automation, but AI agents represent a fundamental shift in how we build adaptable, decision-making systems. Rather than relying on deterministic flows, AI agents use models and contextual data to make decisions dynamically—whether in customer support, autonomous vehicles, or software orchestration. Content:

This paradigm is powered by reinforcement learning, large language models (LLMs), and multi-agent collaboration. AI agents can independently evaluate goals, prioritize tasks, and respond to changing conditions without requiring a full rewrite of logic. For developers, this means less brittle code and more resilient systems.

In applications like workflow automation or digital assistants, integrating AI agents allows systems to "reason" through options and select optimal actions. This flexibility opens up new possibilities for adaptive systems that can evolve over time.

You can explore more practical applications and development frameworks on this AI agents service page.

When designing AI agents, define clear observation and action spaces—this improves interpretability and debugging during development.

[edutech-brijesh]

Machine learning algorithms use data to make predictions and decisions without explicit programming, enabling automation and insights for various applications like healthcare and finance.

[govindhtech]

What is QML? How Can QML Serve as a Tool to Strengthen QKD

How Can Quantum Machine Learning Improve Quantum Key Distribution?

The QML definition

QML solves issues that traditional computers cannot using machine learning and quantum computing. Quantum mechanical ideas like superposition and entanglement may speed up data processing and analysis. QML can generate novel quantum-based algorithms or improve machine learning models.

Key Ideas:

Quantum computing uses qubits, which can be 0 or 1. This allows parallel processing and possibly faster computation for particular jobs.

Machine Learning: Prediction and decision-making using data.

QML blends the two by improving machine learning algorithms with quantum principles or running them on quantum computers.

QML, an interdisciplinary field that blends classical machine learning with quantum computing, will improveQuantum key distribution (QKD), a critical aspect of secure quantum communication systems. QML may improve quantum cryptography protocols' scalability, performance, and dependability in practice, according to recent studies. Data encoding and hardware limits hinder QML integration, which is relatively young.

The most useful use of quantum cryptography is QKD, which uses quantum physics rather than mathematical complexity to revolutionise secure communications. QKD enables two parties to create and exchange a private encryption key over a quantum channel, detecting eavesdropping. This detection capacity is enabled by QKD approaches' quantum particle disruption alerts while measuring or intercepting quantum particles like photons.

A study argues QML supports QKD in several crucial ways:

Improved State Selection and Error Reduction: QML algorithms can help choose quantum states for transmission by avoiding error-prone setups and repeated measurements.

Real-Time Anomaly Detection: QML models like quantum neural networks or quantum-enhanced classifiers can detect tampering or eavesdropping efforts by detecting deviations in predicted patterns like quantum bit error rates or transmission timing.

Optimising Protocols: QML can construct adaptive QKD protocols that adjust operating parameters to channel circumstances using reinforcement learning.

QML fixes side-channel weaknesses in physical implementations and improves quantum random number generators, which generate keys, in efficiency and unpredictability.

QML has several uses beyond QKD and quantum cryptography subjects such safe multi-party computation and homomorphic encryption. It may improve neural network training, reduce dimensionality using principal component analysis, create realistic data, speed up classification operations, find detailed patterns with Boltzmann machines, and cluster high-dimensional datasets. QML can also improve natural language processing, imaging, anomaly detection, supply chain and financial portfolio optimisation, molecular modelling for drug discovery and material development, and autonomous system policy optimisation.

Industry applications include energy grid optimisation, manufacturing scheduling, retail demand forecasting, financial risk management, public health modelling, aerospace trajectory optimisation, environmental modelling, healthcare diagnosis support, cybersecurity threat identification, and manufacturing scheduling.

QML relies on quantum computers to analyse big machine learning datasets. QML processes data faster using quantum principles like superposition and entanglement and qubits' sophisticated information encoding. This could lead to faster ML model training, better model training, and the chance to evaluate quantum-based ML algorithms. Quantum computers can see more complicated data patterns and calculate faster and with less energy.

Combining QML with QKD has challenges, despite its potential:

Current quantum hardware is unstable and unable to scale many QML algorithms.

Classical data conversion to quantum forms for processing is computationally expensive and error-prone.

Complexity, synchronisation issues, and latency result from combining conventional and quantum components.

Model Optimisation: Many QML models are updated from classical approaches, requiring more tailored quantum-native designs.

Algorithm Limitations: Quantum algorithms need more development to outperform conventional ones.

Limited Data and Integrations: QML lacks standardised integration methods with existing IT infrastructures, worsening data quality issues.

Researchers recommend creating QML frameworks tailored for cryptography applications that can run on noisy intermediate-scale quantum (NISQ) devices.

QML may improve quantum network robustness and flexibility as they evolve. QML's ability to manage distributed systems, diagnose issues, and optimise resource distribution will be vital in the future. QML could bridge the gap between scalable, secure infrastructure and fundamental physical principles in the quantum future to secure digital communication.

[damilola-doodles]

🚗Project Title: Smart Retail Shelf Inventory Monitoring, Prediction, and Dynamic Replenishment System. 🎈🎈🎈

ai-ml-ds-retail-inventory-cv-rl-025 Filename: smart_shelf_replenishment.py (Main orchestrator), shelf_cv_module.py (CV interface), replenishment_rl_env.py (RL Environment), rl_agent.py (Agent interface) Timestamp: Mon Jun 02 2025 19:50:53 GMT+0000 (Coordinated Universal Time) Problem Domain:Retail Operations, Supply Chain Management, Inventory Control, Computer Vision, Predictive Analytics,…

[dammyanimation]

🚗Project Title: Smart Retail Shelf Inventory Monitoring, Prediction, and Dynamic Replenishment System. 🎈🎈🎈

ai-ml-ds-retail-inventory-cv-rl-025 Filename: smart_shelf_replenishment.py (Main orchestrator), shelf_cv_module.py (CV interface), replenishment_rl_env.py (RL Environment), rl_agent.py (Agent interface) Timestamp: Mon Jun 02 2025 19:50:53 GMT+0000 (Coordinated Universal Time) Problem Domain:Retail Operations, Supply Chain Management, Inventory Control, Computer Vision, Predictive Analytics,…

[damilola-ai-automation]

🚗Project Title: Smart Retail Shelf Inventory Monitoring, Prediction, and Dynamic Replenishment System. 🎈🎈🎈

ai-ml-ds-retail-inventory-cv-rl-025 Filename: smart_shelf_replenishment.py (Main orchestrator), shelf_cv_module.py (CV interface), replenishment_rl_env.py (RL Environment), rl_agent.py (Agent interface) Timestamp: Mon Jun 02 2025 19:50:53 GMT+0000 (Coordinated Universal Time) Problem Domain:Retail Operations, Supply Chain Management, Inventory Control, Computer Vision, Predictive Analytics,…

[damilola-warrior-mindset]

🚗Project Title: Smart Retail Shelf Inventory Monitoring, Prediction, and Dynamic Replenishment System. 🎈🎈🎈

ai-ml-ds-retail-inventory-cv-rl-025 Filename: smart_shelf_replenishment.py (Main orchestrator), shelf_cv_module.py (CV interface), replenishment_rl_env.py (RL Environment), rl_agent.py (Agent interface) Timestamp: Mon Jun 02 2025 19:50:53 GMT+0000 (Coordinated Universal Time) Problem Domain:Retail Operations, Supply Chain Management, Inventory Control, Computer Vision, Predictive Analytics,…

[damilola-moyo]

🚗Project Title: Smart Retail Shelf Inventory Monitoring, Prediction, and Dynamic Replenishment System. 🎈🎈🎈

ai-ml-ds-retail-inventory-cv-rl-025 Filename: smart_shelf_replenishment.py (Main orchestrator), shelf_cv_module.py (CV interface), replenishment_rl_env.py (RL Environment), rl_agent.py (Agent interface) Timestamp: Mon Jun 02 2025 19:50:53 GMT+0000 (Coordinated Universal Time) Problem Domain:Retail Operations, Supply Chain Management, Inventory Control, Computer Vision, Predictive Analytics,…

[cizotech]

🤖 How does AI learn like humans?

It’s called Reinforcement Learning — reward the good, correct the bad.

This helps AI reason through complex problems, not just follow rules.

It's a key step toward building AGI (Artificial General Intelligence)!

🚀 Want to dive deeper into how it works?

👉 Visit CIZO more mind-blowing AI insights!

[bharatpatel1061]

Safety Constraints in Reinforcement Learning-Based Agents

Reinforcement Learning (RL) empowers agents to learn optimal behavior through trial and error—but without constraints, this can lead to unsafe exploration.

To ensure safety, developers integrate constraints using techniques like:

Constrained policy optimization

Reward shaping

Shielding, where a safety layer filters out dangerous actions

Simulation-first training, avoiding high-risk scenarios in real life

This is especially important in robotics, autonomous vehicles, and healthcare applications, where errors have real-world impact.

The AI agents guide covers architectures and training loops that integrate safety from the ground up.

Pro Tip: Never rely solely on reward functions to encode safety—they're often insufficient without hard boundaries.

[ai-network]

Ultra Mobility Vehicle (UMV): RAI Institute's Robotic Bike

The Real Life Excitebike by RAI Institute

The Robotics and AI Institute (RAI Institute), known for pioneering innovations in robotics and artificial intelligence, has recently unveiled the Ultra Mobility Vehicle (UMV), a groundbreaking robotic bike capable of balancing without traditional gyroscopic technology. Leveraging the power of reinforcement learning, the UMV sets a new benchmark for adaptive robotic mobility, demonstrating capabilities previously unseen in similar devices. Introduction to the Ultra Mobility Vehicle (UMV) Unlike conventional self-balancing bikes that rely on heavy and complex gyroscopes, the UMV achieves stability through a sophisticated yet lightweight mechanism involving dynamic adjustments of a weighted top section and precise steering of its front wheel. This advancement represents a significant leap forward, potentially reshaping the future of robotic transportation and exploration. How Does the UMV Achieve Balance Without a Gyroscope? The core of the UMV's impressive balancing act is its use of reinforcement learning (RL), a specialized machine-learning technique. RL enables the UMV to continuously improve its stability and maneuverability by interacting with its environment, receiving instant feedback, and optimizing its responses over time. Instead of traditional gyroscopes or complex stabilization systems, the UMV's mechanism revolves around two primary actions: - Steering Adjustments: Precision steering through the front wheel helps maintain directional stability. - Dynamic Weight Shifting: An adjustable weighted top section shifts vertically, mimicking human-like balancing actions. This dual-action strategy allows the UMV to respond rapidly to real-world conditions, adjusting seamlessly to changes in terrain and rider demands.   Impressive Capabilities and Versatile Performance The RAI Institute's UMV doesn't just balance-it excels in performing complex and dynamic maneuvers that highlight its versatility: - Terrain Adaptability: The UMV effortlessly navigates challenging and uneven terrains, a capability essential for rugged outdoor environments or hazardous exploration sites​. - Advanced Jumping Mechanics: Utilizing an articulated arm mechanism, the UMV can jump onto elevated surfaces, expanding its usability in complex urban or industrial settings​. - Backward Riding Stability: One of its standout features, backward riding-highly challenging for traditional control methods-is efficiently managed by reinforcement learning, ensuring consistent performance even on unstable grounds​. - Stunt and Trick Execution: From performing wheelies to executing a "track-stand" (a stationary balance position), the UMV demonstrates a wide range of skills valuable for entertainment and demonstration purposes​. The UMV's performance is not just theoretical-it has been effectively demonstrated in controlled tests and demonstrations documented by the RAI Institute.   The UMV Training Process: From Simulation to Reality Developing the UMV involved a rigorous, multi-stage process ensuring reliability and performance consistency: 1. Simulation-Based Training Initial training of the UMV took place in virtual simulations, allowing it to develop basic balancing skills and maneuvering capabilities without physical risk​. 2. Real-World Testing Following successful simulation, real-world testing was conducted to validate and further refine the UMV's skills, ensuring the vehicle could adapt to real-life physical constraints and unpredictability's​. 3. Data Integration A continuous loop of data from real-world tests was integrated back into the simulations, bridging the gap between virtual and physical environments. This iterative improvement cycle significantly enhanced the UMV's performance and adaptability​.   Potential Applications and Future Impact The UMV technology has vast implications across several industries, notably: - Logistics and Delivery: The UMV's agility and terrain adaptability make it ideal for transporting goods in challenging or congested environments, such as warehouses, urban centers, or disaster relief scenarios. - Exploration and Hazardous Environments: The bike's ability to navigate and adapt autonomously is valuable for exploring remote or dangerous areas, such as disaster sites or extraterrestrial landscapes. - Entertainment and Demonstrations: With its capacity to perform visually captivating stunts and maneuvers, the UMV could revolutionize entertainment venues, live events, and promotional demonstrations. These potential uses underscore the versatility and practicality of reinforcement learning in robotic design, possibly leading to lighter, smarter, and more capable robotic systems.   Addressing Technical Challenges: RL vs. MPC One of the UMV's most challenging tasks-riding backward on uneven surfaces-highlights the advantages of reinforcement learning over traditional control methods like Model Predictive Control (MPC). Where MPC struggles to maintain stability under such complex conditions, RL thrives, enabling the UMV to remain balanced and responsive​. RAI Institute Conclusion: Reinforcement Learning Paves the Way Forward The UMV by RAI Institute represents a transformative shift in robotic mobility, demonstrating the powerful capabilities enabled by reinforcement learning. By successfully eliminating gyroscopic dependency, this technology has paved the way for the next generation of lightweight, adaptive, and highly capable robots. As research and development continue, we can anticipate increasingly sophisticated robotics, impacting sectors such as logistics, exploration, entertainment, and beyond. The UMV isn't just a technical breakthrough; it's a clear indication of the vast potential awaiting in the integration of AI-driven learning methods with robotics.   Read the full article

[govindhtech]

Qwen 3 Benchmarks Surpassing Gemini 2.5 Pro, and Grok-3

After four months, Alibaba's new model family may surpass DeepSeek-R1, the top open-weights big language model.

Qwen 3: Faster, Deeper

Overview

Qwen3 is the latest big language model from Qwen. Qwen3-235B-A22B flagship model exceeds DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro in math, coding, and general capabilities. A tiny MoE model, Qwen3-30B-A3B, beats QwQ-32B with ten times as many active parameters, and even Qwen3-4B can compete with Qwen2.5-72B-Instruct.

We are open-weighting two MoE models: Qwen3-235B-A22B, a big model with 235 billion total parameters and 22 billion activated parameters, and Qwen3-30B-A3B, a smaller model with 30 billion total parameters and 3 billion activated parameters.

Six dense models—Qwen3-32B, Qwen3-14B, Qwen3-8B, Qwen3-4B, Qwen3-1.7B, and Qwen3-0.6B—are also open-weighted under Apache 2.0.

Hugging Face, ModelScope, and Kaggle now provide post-trained and pre-trained models like Qwen3-30B-A3B-Base. It recommends SGLang and vLLM for deployment. Ollama, LMStudio, MLX, llama.cpp, and KTransformers are recommended for local usage. These solutions make Qwen3 easy to integrate into development, production, and research workflows.

Qwen 3 allows researchers, developers, and organisations worldwide to design unique solutions using these cutting-edge models.

Try Qwen3 on the mobile app and chat.qwen.ai!

Important Features

Mixed Thinking

Qwen3 models introduce hybrid problem-solving. They offer two modes:

Thinking Mode: The model deliberates before responding. This is ideal for complex topics that require more thought.

Non-Thinking Mode: The model replies almost rapidly, making it suitable for simpler questions where depth is less important than speed.

As previously established, Qwen 3 delivers smooth and scalable performance benefits connected to computational reasoning budget. This design makes task-specific budgets easier to configure, improving inference quality and cost.

Supports several languages

Qwen 3 models accommodate 119 dialects. Due to their multilingual capabilities, these models may be used worldwide, opening up new possibilities.

Increased Agentic Capability

It optimised Qwen 3 models for coding and agentic capabilities and strengthened MCP support. The following examples show how Qwen3 thinks and acts.

In comparison to Qwen2.5

Qwen3 has a much larger pretraining dataset than Qwen2.5. Qwen2.5 was pre-trained on 18 trillion tokens, whereas Qwen3 uses 36 trillion over 119 languages and dialects. Qwen2.5-VL applied these research to enhance it. To add math and code data, Qwen2.5-Math and Qwen2.5-Coder developed synthetic data. Code samples, textbooks, and Q&As are included.

Qwen3 Pre-workout

It takes three stages to prepare for training. The model was pretrained on about 30 trillion tokens with a 4K context length in stage 1 (S1). The model learnt basic language and general knowledge at this time. In stage 2 (S2), we added STEM, coding, and reasoning challenges to the dataset. The model was pretrained with 5 trillion extra tokens. High-quality long-context data was used to extend the context to 32K tokens in the last stage. This assures the model can efficiently handle longer inputs.

Qwen 3 dense base models perform similarly to Qwen2.5 base models with more parameters due to model architectural advancements, more training data, and more efficient training methods. Qwen2.5-3B/7B/14B/32B/72B-Base and Qwen3-1.7B/4B/8B/14B/32B-Base work similarly. Qwen 3 dense base models outperform Qwen2.5 models in STEM, coding, and reasoning. For Qwen3-MoE basis models, they perform similarly to Qwen2.5 dense base models with 10% of active parameters. Thus, training and inference costs drop dramatically.

Post-training

The hybrid model, which can reason step-by-step and respond swiftly, was trained using a four-stage pipeline. This pipeline includes reasoning-based reinforcement learning (RL), thinking mode fusion, long chain-of-thought (CoT) cold start, and generic RL.

First, it improved the models using lengthy CoT data from coding, maths, logical reasoning, and STEM issues. Teaching the model fundamental thinking was the goal. The second phase increased reinforcement learning computing power using rule-based incentives to better model exploration and exploitation.

The third phase enhanced the thinking model utilising extended CoT data and regularly used instruction-tuning data to include non-thinking skills. The second stage's upgraded thinking model produced this data, ensuring smooth reasoning and rapid reaction times. The fourth step employed reinforcement learning (RL) on over 20 broad-domain tasks to increase the model's general capabilities and repair undesired behaviours. Agent capabilities, format following, and instruction following were among these duties.

Agentic uses

Qwen 3 calls tools well. To fully exploit Qwen3's agentic features, use Qwen-Agent. Qwen-Agent's inherent encapsulation of tool-calling templates and parsers simplifies development.

The MCP configuration file, Qwen-Agent integrated tool, or custom tools can define available tools.

[damilola-doodles]

📌Project Title: Integrated Multivariate Financial Forecasting and Deep Reinforcement Learning for Dynamic Portfolio Optimization.🔴

ai-ml-ds-finance-forecast-optim-drl-009 Filename: multivariate_forecasting_portfolio_optimization_drl.py Timestamp: Mon Jun 02 2025 19:22:33 GMT+0000 (Coordinated Universal Time) Problem Domain:Quantitative Finance, Algorithmic Trading, Portfolio Management, Time Series Forecasting, Deep Reinforcement Learning. Project Description:This project constructs an advanced system that integrates…

[dammyanimation]

📌Project Title: Integrated Multivariate Financial Forecasting and Deep Reinforcement Learning for Dynamic Portfolio Optimization.🔴

ai-ml-ds-finance-forecast-optim-drl-009 Filename: multivariate_forecasting_portfolio_optimization_drl.py Timestamp: Mon Jun 02 2025 19:22:33 GMT+0000 (Coordinated Universal Time) Problem Domain:Quantitative Finance, Algorithmic Trading, Portfolio Management, Time Series Forecasting, Deep Reinforcement Learning. Project Description:This project constructs an advanced system that integrates…

[damilola-ai-automation]

📌Project Title: Integrated Multivariate Financial Forecasting and Deep Reinforcement Learning for Dynamic Portfolio Optimization.🔴

ai-ml-ds-finance-forecast-optim-drl-009 Filename: multivariate_forecasting_portfolio_optimization_drl.py Timestamp: Mon Jun 02 2025 19:22:33 GMT+0000 (Coordinated Universal Time) Problem Domain:Quantitative Finance, Algorithmic Trading, Portfolio Management, Time Series Forecasting, Deep Reinforcement Learning. Project Description:This project constructs an advanced system that integrates…