  In meta-reinforcement learning, an agent is trained in multiple different
environments and attempts to learn a meta-policy that can efficiently adapt to
a new environment. This paper presents RAMP, a Reinforcement learning Agent
using Model Parameters that utilizes the idea that a neural network trained to
predict environment dynamics encapsulates the environment information. RAMP is
constructed in two phases: in the first phase, a multi-environment
parameterized dynamic model is learned. In the second phase, the model
parameters of the dynamic model are used as context for the multi-environment
policy of the model-free reinforcement learning agent.
