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Quantifying generalization in reinforcement learning
We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle in reinforcement learning. CoinRun strikes a desirable balance in complexity: the environment is simpler than traditional platformer games like Sonic the Hedgehog but still poses a worthy generalization challenge for state of the art algorithms.
Quantifying generalization in reinforcement learning
We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle in reinforcement learning. CoinRun strikes a desirable balance in complexity: the environment is simpler than traditional platformer games like Sonic the Hedgehog but still poses a worthy generalization challenge for state of the art algorithms.
Coordinated Inauthentic Behavior Explained - meta.com
Coordinated Inauthentic Behavior Explained meta.com
Response to Six4Three Documents - meta.com
Response to Six4Three Documents meta.com
Extended Service Agreement Subscription - Tesla
Extended Service Agreement Subscription Tesla
Tesla Account Support - Tesla
Tesla Account Support Tesla
Winter Driving Tips - Tesla
Winter Driving Tips Tesla
How Are We Doing at Enforcing Our Community Standards? - meta.com
How Are We Doing at Enforcing Our Community Standards? meta.com
Amazon selects New York City and Northern Virginia for new headquarters - About Amazon
Amazon selects New York City and Northern Virginia for new headquarters About Amazon
More Information About Last Week’s Takedowns - meta.com
More Information About Last Week’s Takedowns meta.com
Spinning Up in Deep RL
We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.
Spinning Up in Deep RL
We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.
Hard Questions: What Are We Doing to Stay Ahead of Terrorists? - meta.com
Hard Questions: What Are We Doing to Stay Ahead of Terrorists? meta.com
Learning concepts with energy functions
We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use concepts learned in a 2d particle environment to solve tasks on a 3-dimensional physics-based robot.
Learning concepts with energy functions
We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use concepts learned in a 2d particle environment to solve tasks on a 3-dimensional physics-based robot.
Election Update - meta.com
Election Update meta.com
An Independent Assessment of the Human Rights Impact of Facebook in Myanmar - meta.com
An Independent Assessment of the Human Rights Impact of Facebook in Myanmar meta.com
An Independent Assessment of the Human Rights Impact of Facebook in Myanmar - meta.com
An Independent Assessment of the Human Rights Impact of Facebook in Myanmar meta.com
Reinforcement learning with prediction-based rewards
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.
Reinforcement learning with prediction-based rewards
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.