yet more formatting

Author: izhigal

docs/high-level-design.md

@@ -25,4 +25,4 @@ Card games usually have similar structures. We abstract some concepts in card ga
 To summarize, in one `Game`, a `Dealer` deals the cards for each `Player`. In each `Round` of the game, a `Judger` will make major decisions about the next round and the payoffs in the end of the game.
 
 ## Agents
-We provide examples of several representative algorithms and wrap them as `Agent` to show how a learning algorithm can be connected to the toolkit. The first example is DQN which is a representative of the Reinforcement Learning (RL) algorithms category. The second example is NFSP which is a representative of the Reinforcement Learning (RL) with cls-play. We also provide CFR (chance sampling) and DeepCFR which belong to Conterfactual Regret Minimization (CFR) category. Other algorithms from these three categories can be connected in similar ways.
+We provide examples of several representative algorithms and wrap them as `Agent` to show how a learning algorithm can be connected to the toolkit. The first example is DQN which is a representative of the Reinforcement Learning (RL) algorithms category. The second example is NFSP which is a representative of the Reinforcement Learning (RL) with self-play. We also provide CFR (chance sampling) and DeepCFR which belong to Counterfactual Regret Minimization (CFR) category. Other algorithms from these three categories can be connected in similar ways.