# Development¶

You can add your own pre-trained/rule-based models to the toolkit by following several steps:

• Develop models. You can either design a rule-based model or save a neural network model. For each game, you need to develop agents for all the players at the same time. You need to wrap each agent as a Agent class and make sure that step, eval_step and use_raw can work correctly.

• Wrap models. You need to inherit the Model class in rlcard/models/model.py. Then put all the agents into a list. Rewrite agent property to return this list.

• Register the model. Register the model in rlcard/models/__init__.py.

• Load the model in environment. An example of loading leduc-holdem-nfsp model is as follows:

from rlcard import models


Then use leduc_nfsp_model.agents to obtain all the agents for the game.

## 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).

To add a new environment to the toolkit, generally you should take the following steps:

• Implement a game. Card games usually have similar structures so that they can be implemented with Game, Round, Dealer, Judger, Player, as in existing games. The easiest way is to inherit the classed in rlcard/games/base.py and implement the functions.

• Wrap the game with an environment. The easiest way is to inherit Env in rlcard/envs/env.py. You need to implement _extract_state which encodes the state, _decode_action which decodes actions from the id to the text string, and get_payoffs which calculates payoffs of the players.

• Register the game. Now it is time to tell the toolkit where to locate the new environment. Go to rlcard/envs/__init__.py, and indicate the name of the game and its entry point.

To test whether the new environment is set up successfully:

import rlcard
rlcard.make(#the new evironment#)


## Customizing Environments¶

In addition to the default state representation and action encoding, we also allow customizing an environment. In this document, we use Limit Texas Hold’em as an example to describe how to modify state representation, action encoding, reward calculation, or even the game rules.

### State Representation¶

To define our own state representation, we can modify the _extract_state function in /rlcard/envs/limitholdem.py.

### Action Encoding¶

To define our own action encoding, we can modify the _decode_action function in /rlcard/envs/limitholdem.py.

### Reward Calculation¶

To define our own reward calculation, we can modify the get_payoffs function in /rlcard/envs/limitholdem.py.

### Modifying Game¶

We can change the parameters of a game to adjust its difficulty. For example, we can change the number of players, the number of allowed raises in Limit Texas Hold’em in the __init__ function in rlcard/games/limitholdem/game.py.