Training a high-dimensional simulated agent with an under-specified reward function often
leads the agent to learn physically infeasible strategies that are ineffective when deployed in …

Rapid progress in deep reinforcement learning has made it increasingly feasible to train
controllers for high-dimensional humanoid bodies. However, methods that use pure …

Reinforcement learning (RL) has emerged as a powerful approach for locomotion control of
highly articulated robotic systems. However, one major challenge is the tedious process of …

Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs,
such as images. However, real-world applications of such methods require generalizing to …

While deep reinforcement learning excels at solving tasks where large amounts of data can
be collected through virtually unlimited interaction with the environment, learning from …

Reward function design and exploration time are arguably the biggest obstacles to the
deployment of reinforcement learning (RL) agents in the real world. In many real-world …

Deep neural network based reinforcement learning (RL) can learn appropriate visual
representations for complex tasks like vision-based robotic grasping without the need for …

Deep reinforcement learning achieves superhuman performance in a range of video game
environments, but requires that a designer manually specify a reward function. It is often …

Deep generative models have recently shown great promise in imitation learning for motor
control. Given enough data, even supervised approaches can do one-shot imitation …

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial
perturbations to their observations, similar to adversarial examples for classifiers. However …