  Meta Reinforcement Learning (RL) methods focus on automating the design of RL
algorithms that generalize to a wide range of environments. The framework
introduced in (Anonymous, 2020) addresses the problem by representing different
RL algorithms as Directed Acyclic Graphs (DAGs), and using an evolutionary meta
learner to modify these graphs and find good agent update rules. While the
search language used to generate graphs in the paper serves to represent
numerous already-existing RL algorithms (e.g., DQN, DDQN), it has limitations
when it comes to representing Policy Gradient algorithms. In this work we try
to close this gap by extending the original search language and proposing
graphs for five different Policy Gradient algorithms: VPG, PPO, DDPG, TD3, and
SAC.
