Learning Robust and Adaptive Real-World Continuous Control Using
  Simulation and Transfer Learning
  We use model-free reinforcement learning, extensive simulation, and transfer
learning to develop a continuous control algorithm that has good zero-shot
performance in a real physical environment. We train a simulated agent to act
optimally across a set of similar environments, each with dynamics drawn from a
prior distribution. We propose that the agent is able to adjust its actions
almost immediately, based on small set of observations. This robust and
adaptive behavior is enabled by using a policy gradient algorithm with an Long
Short Term Memory (LSTM) function approximation. Finally, we train an agent to
navigate a two-dimensional environment with uncertain dynamics and noisy
observations. We demonstrate that this agent has good zero-shot performance in
a real physical environment. Our preliminary results indicate that the agent is
able to infer the environmental dynamics after only a few timesteps, and adjust
its actions accordingly.
