Pulse

OpenAI and DeepMind have developed a new algorithm that learns desired behaviors from human feedback, reducing the need for explicit goal functions. This method uses a three-step cycle where humans compare two agent behaviors, allowing the AI to infer the reward function and improve its performance. The approach has shown promising sample efficiency, requiring minimal human input to learn complex tasks like a backflip, and has achieved strong results in simulated robotics and Atari games, sometimes surpassing performance with standard reward functions. However, the system can be susceptible to agents that trick human evaluators, a problem being addressed with additional visual cues. AI

OpenAI researchers are exploring the transferability of adversarial robustness across different types of perturbations in neural networks. Their findings indicate that robustness against one perturbation type does not always guarantee robustness against others and can sometimes be detrimental. They recommend evaluating adversarial defenses using a diverse range of perturbation types and sizes to ensure comprehensive security. Additionally, OpenAI is investigating adversarial examples as a concrete AI safety problem, noting their potential to cause significant issues, such as tricking autonomous vehicles. AI

OpenAI has published a series of research papers detailing advancements in reinforcement learning. These include achieving superhuman performance in Dota 2 with OpenAI Five, developing benchmarks for safe exploration in RL, and quantifying generalization capabilities with the CoinRun environment. The company also explored novel methods like prediction-based rewards for curiosity-driven exploration, learning policy representations in multiagent systems, and an experimental metalearning approach called Evolved Policy Gradients for faster training on new tasks. Further research addresses variance reduction in policy gradients and the equivalence between policy gradients and soft Q-learning, alongside challenging robotics environments for multi-goal RL. AI