Creating Reinforcement Learning agents on rlcard enviroment

New limit holdem game

A limit holdem mode with shorter deck 4x(A, 10, J, Q, K), 1 hand card, 2 public cards

Purpose: Shorter state space, test simplier algorithms

Threshold Agent and Threshold Agent2:

Rule based models betting only on high cards and combinations

new_limit_holdem_human: play againt any suitable agent

Algorithms Implemented

Phase 1 (new limit holdem):

Q-learning variation algorithm: ql_agent(QLAgent)

policy iteration algorithm: pi_agent(PIAGENT)

SARSA algorithm: sarsa_agent(SARSAAgent)

Phase 2 (Full limit holdem game using Neural Networks):

Double DQN Agent: double_dqn_agent(DoubleDQNAgent):

Network architecture:

Dueling Double DQN Agent: dueling_double_dqn_agent(DDDQNAgentV2)

Network architecture:

State Represatation used (inspired by Alpha Holdem):

Testing results vs Bluf Thresholf model ( model desinged to train agents ):

Currently working on optimizing our models and later on adding convolutional networks and prioritized experience replay.