Reinforcement Learning

UC Berkeley Researchers Introduce Video Prediction Rewards (VIPER): An Algorithm That Leverages Pretrained Video Prediction Models As Action-Free Reward Signals For Reinforcement Learning

Designing a reward function by hand is time-consuming and can result in unintended consequences. This is a major roadblock in developing reinforcement learning (RL)-based...

Meet MACTA: An Open-Sourced Multi-Agent Reinforcement Learning Approach for Cache Timing Attacks and Detection

We are deluged with multiple forms of data. Be it data from a financial sector, healthcare, educational sector, or an organization. Privacy and security...

5 Reasons Why Large Language Models (LLMs) Like ChatGPT Use Reinforcement Learning Instead of Supervised Learning for Finetuning

With the huge success of Generative Artificial Intelligence in the past few months, Large Language Models are continuously advancing and improving. These models are...

Do You Really Need Reinforcement Learning (RL) in RLHF? A New Stanford Research Proposes DPO (Direct Preference Optimization): A Simple Training Paradigm For Training...

When trained on massive datasets, huge unsupervised LMs acquire powers that surprise even their creators. These models, however, are trained on information produced by...

A New Deep Reinforcement Learning (DRL) Framework can React to Attackers in a Simulated Environment and Block 95% of Cyberattacks Before They Escalate

Cybersecurity defenders must dynamically adapt their techniques and tactics as technology develops and the level of complexity in a system surges. As machine learning...

UC Berkeley Researchers Propose FastRLAP: A System for Learning High-Speed Driving via Deep RL (Reinforcement Learning) and Autonomous Practicing

Researchers from the University of California, Berkeley, have developed a system called FastrLap that uses machine learning to teach autonomous vehicles to drive aggressively...

Superhuman Performance on the Atari 100K Benchmark: The Power of BBF – A New Value-Based RL Agent from Google DeepMind, Mila, and Universite de...

Deep reinforcement learning (RL) has emerged as a powerful machine learning algorithm for tackling complex decision-making tasks. To overcome the challenge of achieving human-level...

DeepMind Introduces AlphaDev: A Deep Reinforcement Learning Agent Which Discovers Faster Sorting Algorithms From Scratch

From Artificial Intelligence and Data Analysis to Cryptography and Optimization, algorithms play an important role in every domain. Algorithms are basically a set of...

Computer Vision Meets 🫠 Reinforcement Learning: This AI Research Shows that Reward Optimization is a Viable Option to Optimize a Variety of Computer Vision...

Not how effectively the model maximizes the training goal, but rather how well the predictions are matched with the task risk, i.e., the model's...

New AI Research From Anthropic Shows That Simple Prompting Approaches Can Help Large Language Models (LLMs) Trained With Reinforcement Learning From Human Feedback (RLHF)...

Big language models show negative social prejudices, which can occasionally grow worse with larger models. Scaling model size can improve model performance on a...

Tracking Odor Plumes With AI Agents Using A Deep Reinforcement Learning Model

The extraordinary talents of animals have long served as a source of inspiration for scientists and engineers who have worked to reverse engineer or...

A New Transformer Based Reinforcement Learning Agent Called ‘AdA’ Inhabits a Rich 3D World and Can Rapidly Adapt to Tasks It Has Never Seen...

It has always been astounding how quickly humans can adjust to their environment. Artificial intelligence agents have been developed over many years to replicate...

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