Supplementary Data for Evolving Reinforcement Learning Algorithms
This dataset contains 1000 loss graphs from two experiments: 500 unique graphs learned from scratch, and 500 unique graphs seeded by the DQN loss.
There are two csv files: from_scratch.csv and dqn_seeded.csv. They have two columns: id and reward. Each file is sorted by reward from highest to lowest. Graph with is visualized in a png file named .png. These graphs are under folders from_scratch_graphs/ and dqn_seeded_graphs/.
Notes on reading the graph:
- Input nodes are in green, the output node is in blue.
- The directed edges represent the data flow. A red edge represents the 2nd input for a binary operator, and all other edges are in black. Such coloring scheme is necesssary for encoding inputs for non-commutative operators like -, /, etc.
- It’s common to have isolated input nodes and intermediate nodes that do not contribute to the final output. We can ignore these nodes.
- As an example, Q(s_{t-1}, a_{t-1}) is represented by 5 nodes:
- Q_param → QValueListOp ← s_tm1. This gives Q(s_{t-1}, -).
- QValueListOp → SelectList ← a_{t-1}. This uses a_{t-1} to index into Q(s_{t-1}, -).
