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OpenAI News
We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge.
Topics: Agents
Entities: Agents
OpenAI News
We’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.
OpenAI News
We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time...
Topics: AgentsPolicyInfrastructure
Entities: AgentsPolicyInfrastructure
OpenAI News
Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the...
Topics: AgentsInfrastructure
Entities: AgentsInfrastructure
OpenAI News
We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborative strategies like tit-for-tat in the iterated prisoner’s dilemma. This algorithm, Learning with Opponent-Learning...
Topics: Agents
Entities: Agents
OpenAI News
Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised...
Topics: AgentsPolicyInfrastructure
Entities: AgentsPolicyInfrastructure
OpenAI News
Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum—the difficulty of the environment is determined by the skill of your...
Topics: Agents
Entities: Agents
OpenAI News
By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact...
Topics: Agents
Entities: Agents
OpenAI News
Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize....
Topics: Agents
Entities: Agents