developping-algorithms.md

Developping Algorithms

Although users may do whatever they like to design and try their algorithms. We recommend wrapping a new algorithm as an Agent class the example agent. To be compatible with the basic interfaces, the agent should have the following functions and attribute:

• step: Given the current state, predict the next action.
• eval_step: Similar to step, but for evaluation purpose. Reinforcement learning algorithms will usually add some noise for better exploration in training. In evaluation, no noise will be added to make predictions.
• use_raw: A boolean attribute. True if the agent uses raw states to do reasoning; False if the agent uses numerical values to play (such as neural networks).