. Conversion wrappers# AEC to Parallel#. Leduc Hold’em is a poker variant that is similar to Texas Hold’em, which is a game often used in academic research []. We also evaluate SoG on the commonly used small benchmark poker game Leduc hold’em, and a custom-made small Scotland Yard map, where the approximation quality compared to the optimal policy can be computed exactly. reset(). The results show that Suspicion-Agent can potentially outperform traditional algorithms designed for imperfect information games, without any specialized. The second round consists of a post-flop betting round after one board card is dealt. Leduc Hold'em is a poker variant where each player is dealt a card from a deck of 3 cards in 2 suits. Leduc Hold'em은 Texas Hold'em의 단순화 된. RLCard is an open-source toolkit for reinforcement learning research in card games. Apart from rule-based collusion, we use Deep Reinforcement Learning (Arulkumaran et al. . . Leduc Hold’em is a variation of Limit Texas Hold’em with fixed number of 2 players, 2 rounds and a deck of six cards (Jack, Queen, and King in 2 suits). doudizhu-rule-v1. . Example implementation of the DeepStack algorithm for no-limit Leduc poker - GitHub - matthewmav/MIB: Example implementation of the DeepStack algorithm for no-limit Leduc pokerLeduc Hold’em; Rock Paper Scissors; Texas Hold’em No Limit; Texas Hold’em; Tic Tac Toe; MPE. This is essentially the same one I am using for my. In PettingZoo, we can use action masking to prevent invalid actions from being taken. . from pettingzoo. public_card (object) – The public card that seen by all the players. A solution to the smaller abstract game can be computed and isReinforcement Learning / AI Bots in Card (Poker) Game: New limit Holdem - GitHub - gsiatras/Reinforcement_Learning-Q-learning_and_Policy_Iteration_Rlcard. [0,1] Gin Rummy is a 2-player card game with a 52 card deck. agents import LeducholdemHumanAgent as HumanAgent. Simple; Simple Adversary; Simple Crypto; Simple Push; Simple Reference; Simple Speaker Listener; Simple Spread; Simple Tag; Simple World Comm; SISL. - rlcard/leducholdem. Toggle navigation of MPE. num_players = 2 ''' # Some configarations of the game # These arguments can be specified for creating new games # Small blind and big blind: self. #. . 2: The 18 Card UH-Leduc-Hold’em Poker Deck. . >> Leduc Hold'em pre-trained model >> Start a new game! >> Agent 1 chooses raise. Environment Setup# To follow this tutorial, you will need to install the dependencies shown below. static judge_game (players, public_card) ¶ Judge the winner of the game. We can know that the Leduc Hold'em environment is a 2-player game with 4 possible actions. The idea. Please read that page first for general information. 5 1 1. Researchers began to study solving Texas Hold’em games in 2003, and since 2006, there has been an Annual Computer Poker Competition (ACPC) at the AAAI Conference on Artificial Intelligence in which poker agents compete against each other in a variety of poker formats. Returns: A dictionary of all the perfect information of the current state. AEC #. import rlcard. . You can try other environments as well. 1, the oil well strike that started Alberta's main oil boom, near Devon, Alberta. Leduc hold'em Poker is a larger version than Khun Poker in which the deck consists of six cards (Bard et al. . (210, 160, 3) Observation Values. 1. Jonathan Schaeffer. . It has 111 channels representing:50 lines (42 sloc) 1. The deck consists only two pairs of King, Queen and Jack, six cards in total. Most environments only give rewards at the end of the games once an agent wins or losses, with a reward of 1 for winning and -1 for losing. Leduc Hold’em : 10^2 : 10^2 : 10^0 : leduc-holdem : 文档, 释例 : 限注德州扑克 Limit Texas Hold'em (wiki, 百科) : 10^14 : 10^3 : 10^0 : limit-holdem : 文档, 释例 : 斗地主 Dou Dizhu (wiki, 百科) : 10^53 ~ 10^83 : 10^23 : 10^4 : doudizhu : 文档, 释例 : 麻将 Mahjong. . utils import average_total_reward from pettingzoo. We support Python 3. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with mul-tiple agents, large state and action space, and sparse reward. This allows PettingZoo to represent any type of game multi-agent RL can consider. In this paper, we propose a safe depth-limited subgame solving algorithm with diverse opponents. If you have any questions, please feel free to ask in the Discord server. . Different environments have different characteristics. Leduc Hold'em is a simplified version of Texas Hold'em. . This tutorial shows how to train a Deep Q-Network (DQN) agent on the Leduc Hold’em environment (AEC). {"payload":{"allShortcutsEnabled":false,"fileTree":{"rlcard/models":{"items":[{"name":"pretrained","path":"rlcard/models/pretrained","contentType":"directory"},{"name. If you get stuck, you lose. Two cards, known as hole cards, are dealt face down to each player, and then five community cards are dealt face up in three stages. But even Leduc hold ’em (27), with six cards, two betting rounds, and a two-bet maxi-mum having a total of 288 information sets, is intractable, having more than 1086 possible de-terministic strategies. It uses pure PyTorch and is written in only ~4000 lines of code. So in total there are 6*h1 + 5*6*h2 information sets, where h1 is the number of hands preflop and h2 is the number of flop/hand pairs on the flop. For learning in Leduc Hold’em, we manually calibrated NFSP for a fully connected neural network with 1 hidden layer of 64 neurons and rectified linear. Many classic environments have illegal moves in the action space. View leduc2. How to Cite Davis, T. Apart from rule-based collusion, we use Deep Re-inforcementLearning[Arulkumaranetal. . 10 and 3. Most environments only give rewards at the end of the games once an agent wins or losses, with a reward of 1 for winning and -1 for losing. . The state (which means all the information that can be observed at a specific step) is of the shape of 36. doc, example. RLCard is an open-source toolkit for reinforcement learning research in card games. The latter is a smaller version of Limit Texas Hold’em and it was introduced in the research paper Bayes’ Bluff: Opponent Modeling in Poker in 2012. Leduc Hold’em 10^2 10^2 10^0 leduc-holdem 文档, 释例 限注德州扑克 Limit Texas Hold'em (wiki, 百科) 10^14 10^3 10^0 limit-holdem 文档, 释例 斗地主 Dou Dizhu (wiki, 百科) 10^53 ~ 10^83 10^23 10^4 doudizhu 文档, 释例 麻将 Mahjong (wiki, 百科) 10^121 10^48 10^2 mahjong 文档, 释例Leduc Hold’em (a simplified Texas Hold’em game), Limit Texas Hold’em, No-Limit Texas Hold’em, UNO, Dou Dizhu and Mahjong. It is played with a deck of six cards, comprising two suits of three ranks each (often the king, queen, and jack - in our implementation, the ace, king, and queen). tbd; Follow me on Twitter to get updates when new parts go live. DeepStack for Leduc Hold'em. . g. static step (state) ¶ Predict the action when given raw state. In addition, we show that static experts can cre-ate strong agents for both 2-player and 3-player Leduc and Limit Texas Hold'em poker, and that a specific class of static experts can be preferred. The game ends if both players sequentially decide to pass. Please read that page first for general information. The Kuhn poker is a one-round poker, where the winner is determined by the highest card. py Go to file Go to file T; Go to line L; Copy path Copy permalink; This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. proposed instant updates. RLCard is an open-source toolkit for reinforcement learning research in card games. Model Explanation; leduc-holdem-cfr: Pre-trained CFR (chance sampling) model on Leduc Hold'em: leduc-holdem-rule-v1: Rule-based model for Leduc Hold'em, v1An attempt at a Python implementation of Pluribus, a No-Limits Hold'em Poker Bot - pluribus/README. python open-source machine-learning artificial-intelligence poker-engine texas-holdem-poker counterfactual-regret-minimization pluribus Resources. to bridge reinforcement learning and imperfect information games. . Smooth UCT, on the other hand, continued to approach a Nash equilibrium, but was eventually overtakenEnvironment Creation. Utility Wrappers: a set of wrappers which provide convenient reusable logic, such as enforcing turn order or clipping out-of-bounds actions. leducholdem_rule_models. from rlcard. The winner will receive +1 as a reward and the loser will get -1. Each game is fixed with two players, two rounds, two-bet maximum and raise amounts of 2 and 4 in the first and second round. . We have also constructed a smaller version of hold ’em, which seeks to retain the strategic ele-ments of the large game while keeping the size of the game tractable. This is a popular way of handling rewards with significant variance of magnitude, especially in Atari environments. Neural Networks. . In a two-player zero-sum game, the exploitability of a strategy profile, π, is. The experiments are conducted on Leduc Hold'em [13] and Leduc-5 [2]. doudizhu. RLcard is an easy-to-use toolkit that provides Limit Hold’em environment and Leduc Hold’em environment. . Waterworld is a simulation of archea navigating and trying to survive in their environment. The comments are designed to help you understand how to use PettingZoo with CleanRL. . Leduc Hold ’Em. For example, in a game of chess, it is impossible to move a pawn forward if it is already at the front of the board. There is no action feature. , 2005] and Flop Hold’em Poker (FHP) [Brown et al. Rule-based model for UNO, v1. ,2012) when compared to established methods like CFR (Zinkevich et al. {"payload":{"allShortcutsEnabled":false,"fileTree":{"tutorials/Ray":{"items":[{"name":"render_rllib_leduc_holdem. uno-rule-v1. Neural network optimtzation of algorithm DeepStack for playing in Leduc Hold’em. You can also find the code in examples/run_cfr. {"payload":{"allShortcutsEnabled":false,"fileTree":{"pettingzoo/classic/rlcard_envs":{"items":[{"name":"font","path":"pettingzoo/classic/rlcard_envs/font. Returns: Each entry of the list corresponds to one entry of the. Many classic environments have illegal moves in the action space. py to play with the pre-trained Leduc Hold'em model. Leduc Hold'em is a toy poker game sometimes used in academic research (first introduced in Bayes' Bluff: Opponent Modeling in Poker). In this paper, we provide an overview of the key components This work centers on UH Leduc Poker, a slightly more complicated variant of Leduc Hold’em Poker. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold’em, Texas Hold’em, UNO, Dou Dizhu and Mahjong. . This tutorial is a full example using Tianshou to train a Deep Q-Network (DQN) agent on the Tic-Tac-Toe environment. Returns: list of payoffs. Training CFR (chance sampling) on Leduc Hold'em . game - this file defines that we are playing the game of Leduc hold'em. In this paper, we provide an overview of the key. using two different heads-up limit poker variations: a small-scale variation called Leduc Hold’em, and a full-scale one called Texas Hold’em. 11. doc, example. 游戏过程很简单, 首先, 两名玩家各投1个筹码作为底注(也有大小盲玩法, 即一个玩家下1个筹码, 另一个玩家下2个筹码). Fictitious Self-Play in Leduc Hold’em 0 0. In Leduc hold ’em, the deck consists of two suits with three cards in each suit. Pursuers also receive a reward of 0. . This environment is part of the MPE environments. We release all interaction data between Suspicion-Agent and traditional algorithms for imperfect-informationin imperfect-information games, such as Leduc Hold’em (Southey et al. In Leduc hold ’em, the deck consists of two suits with three cards in each suit. The AEC API supports sequential turn based environments, while the Parallel API. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong. Training CFR on Leduc Hold'em; Having Fun with Pretrained Leduc Model; Training DMC on Dou Dizhu; Contributing. py. 1 Strategic Decision Making . Each walker receives a reward equal to the change in position of the package from the previous timestep, multiplied by the forward_reward scaling factor. The game is over when the ball goes out of bounds from either the left or right edge of the screen. Parameters: players (list) – The list of players who play the game. 10^3. . Python implement of DeepStack-Leduc. agents: # this is where you would insert your policy actions = {agent: env. Cannot retrieve contributors at this time. md","contentType":"file"},{"name":"best_response. Rules can be found here. PettingZoo Wrappers#. Note you can easily find yourself in a dead-end escapable only through the. Demo. 1 in Figure 5. (0,255) Entombed’s competitive version is a race to last the longest. In the rst round a single private card is dealt to each. Simple; Simple Adversary; Simple Crypto; Simple Push; Simple Reference; Simple Speaker Listener; Simple Spread; Simple Tag; Simple World Comm; SISL. 2. Pre-trained CFR (chance sampling) model on Leduc Hold’em. (0, 255) This is a simple physics based cooperative game where the goal is to move the ball to the left wall of the game border by activating the vertically moving pistons. mahjong¶ class rlcard. We will walk through the creation of a simple Rock-Paper-Scissors environment, with example code for both AEC and Parallel environments. DQN for Simple Poker Train a DQN agent in an AEC environment. env = rlcard. Most of the strong poker AI to date attempt to approximate a Nash equilibria to one degree. '''. At the beginning of the game, each player receives one card and, after betting, one public card is revealed. 52 cards; Each player has 2 hole cards (face-down cards)Having Fun with Pretrained Leduc Model. Additionally, we show that SES isContribute to xiviu123/rlcard development by creating an account on GitHub. games: Leduc Hold’em [Southey et al. It supports various card environments with easy-to-use interfaces, including Blackjack, Leduc Hold’em, Texas Hold’em, and many more. RLcard is an easy-to-use toolkit that provides Limit Hold’em environment and Leduc Hold’em environment. . . . Similarly, an information state of Leduc Hold’em can be encoded as a vector of length 30, as it contains 6 cards with 3 duplicates, 2 rounds, 0 to 2 raises per round and 3 actions. We have shown, it is a hard task to nd global optima for Stackelberg equilibrium, even the three-player Kuhn Poker. A few years back, we released a simple open-source CFR implementation for a tiny toy poker game called Leduc hold'em link. This tutorial will demonstrate how to use LangChain to create LLM agents that can interact with PettingZoo environments. 10^3. 为此，东京大学的研究人员引入了Suspicion Agent这一创新智能体，通过利用GPT-4的能力来执行不完全信息博弈。. These tutorials show you how to use Ray’s RLlib library to train agents in PettingZoo environments. . It is played with 6 cards: 2 Jacks, 2 Queens, and 2 Kings. This program is evaluated using two different heads-up limit poker variations: a small-scale variation called Leduc Hold’em, and a full-scale one called Texas Hold’em. PettingZoo is a simple, pythonic interface capable of representing general multi-agent reinforcement learning (MARL) problems. Contribute to achahalrsh/rlcard-getaway development by creating an account on GitHub. The work in this thesis explores the task of learning how an opponent plays and subsequently coming up with a counter-strategy that can exploit that information, using. . LeducHoldemRuleAgentV1 ¶ Bases: object. We will also introduce a more flexible way of modelling game states. agents} observations, rewards,. CleanRL is a lightweight,. The RLCard toolkit supports card game environments such as Blackjack, Leduc Hold’em, Dou Dizhu, Mahjong, UNO, etc. . This amounts to the first action abstraction algorithm (algo-rithm for selecting a small number of discrete actions to use from a continuum of actions—a key preprocessing step forSolving Leduc Hold’em Counterfactual Regret Minimization; From aerospace guidance to COVID-19: Tutorial for the application of the Kalman filter to track COVID-19; A Reinforcement Learning Algorithm for Recycling Plants; Monte Carlo Tree Search with Repetitive Self-Play for Tic-Tac-Toe; Developing a Decision Making Agent to Play RISK;. Contribute to mpgulia/rlcard-getaway development by creating an account on GitHub. Also added support for num_players in RLcard based environments which can have variable numbers of players. Contribution to this project is greatly appreciated! Please create an issue/pull request for feedbacks or more tutorials. Confirming the observations of [Ponsen et al. Leduc Hold'em is a simplified version of Texas Hold'em. , 2019]. Limit Texas Hold’em (wiki, baike) 10^14. The ε-greedy policies’ exploration started at 0. Deep Q-Learning (DQN) (Mnih et al. . It supports various card environments with easy-to-use interfaces, including. Firstly, tell “rlcard” that we need a Leduc Hold’em environment. The first computer program to outplay human professionals at heads-up no-limit Hold'em poker. , 2005] and Flop Hold’em Poker (FHP) [Brown et al. Entombed’s cooperative version is an exploration game where you need to work with your teammate to make it as far as possible into the maze. UH-Leduc Hold’em Deck: This is a “ queeny ” 18-card deck from which we draw the players’ card sand the flop without replacement. Fictitious Self-Play in Leduc Hold’em 0 0. Dirichlet distributions offer a simple prior for multinomi- 6 Experimental Setup als, which is a. RLCard is an open-source toolkit for reinforcement learning research in card games. To follow this tutorial, you will need to install the dependencies shown below. Rules can be found here. . There is a two bet maximum per round, with raise sizes of 2 and 4 for each round. consider a simplifed version of poker called Leduc Hold’em; again we show that purification leads to a significant perfor-mance improvement over the standard approach, and fur-thermore that whenever thresholding improves a strategy, the biggest improvement is often achieved using full purifi-cation. . Contents 1 Introduction 12 1. . We will go through this process to have fun! Leduc Hold’em is a variation of Limit Texas Hold’em with fixed number of 2 players, 2 rounds and a deck of six cards (Jack, Queen, and King in 2 suits). . There are two rounds. Leduc Hold'em is a simplified version of Texas Hold'em. Leduc Hold'em is a simplified version of Texas Hold'em. Combat ’s plane mode is an adversarial game where timing, positioning, and keeping track of your opponent’s complex movements are key. Texas hold 'em (also known as Texas holdem, hold 'em, and holdem) is one of the most popular variants of the card game of poker. 185, Section 5. We investigate the convergence of NFSP to a Nash equilibrium in Kuhn poker and Leduc Hold’em games with more than two players by measuring the exploitability rate of learned strategy profiles. The same to step. Simple; Simple Adversary; Simple Crypto; Simple Push; Simple Reference; Simple Speaker Listener; Simple Spread; Simple Tag; Simple World Comm; SISL. RLCard is an open-source toolkit for reinforcement learning research in card games. 1 Contributions . 140 FollowersLeduc Hold’em; Rock Paper Scissors; Texas Hold’em No Limit; Texas Hold’em; Tic Tac Toe; MPE. Leduc Hold ‘em Rule agent version 1. The code was written in the Ruby Programming Language. Having fun with pretrained Leduc model; Leduc Hold'em as single-agent environment; Training CFR on Leduc Hold'em; Demo. Poker. In addition to NFSP’s main, average strategy profile we also evaluated the best response and greedy-average strategies, which deterministically choose actions that maximise the predicted ac- tion values or probabilities respectively. Leduc Hold’em is a two-round game with the winner determined by a pair or the highest card. Our method can successfully detect co-Tic Tac Toe. We perform numerical experiments on scaled-up variants of Leduc hold’em , a poker game that has become a standard benchmark in the EFG-solving community, as well as a security-inspired attacker/defender game played on a graph. The latter is a smaller version of Limit Texas Hold’em and it was introduced in the research paper Bayes’ Bluff: Opponent Modeling in Poker in 2012. If you find this repo useful, you may cite:Update rlcard to v1. butterfly import pistonball_v6 env = pistonball_v6. big_blind = 2 * self. The game we will play this time is Leduc Hold’em, which was first introduced in the 2012 paper “ Bayes’ Bluff: Opponent Modelling in Poker ”. . using two different heads-up limit poker variations: a small-scale variation called Leduc Hold’em, and a full-scale one called Texas Hold’em. ipynb","path. After betting, three community cards. Action masking is a more natural way of handling invalid. Leduc Hold’em. We evaluate SoG on four games: chess, Go, heads-up no-limit Texas hold’em poker, and Scotland Yard. /example_player we specified leduc. . At the beginning of a hand, each player pays a one chip ante to the pot and receives one private card. . py to play with the pre-trained Leduc Hold'em model. Training CFR on Leduc Hold'em. Leduc Formation, a stratigraphical unit in the Western Canadian Sedimentary Basin. Each pursuer observes a 7 x 7 grid centered around itself, depicted by the orange boxes surrounding the red pursuer agents. #Leduc Hold'em is a simplified poker game in which each player gets 1 card. Using Response Functions to Measure Strategy Strength. Run examples/leduc_holdem_human. Tianshou: Basic API Usage#. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"experiments","path":"experiments","contentType":"directory"},{"name":"models","path":"models. Written by Thomas Trenner. . . Figure 1 shows the exploitability rate of the profile of NFSP in Kuhn poker games with two, three, four, or five. . small_blindjack, Leduc Hold’em, Texas Hold’em, UNO, Dou Dizhu and Mahjong. 1 Contributions . Training CFR (chance sampling) on Leduc Hold'em . Thus, any single-agent algorithm can be connected to the environment. md#leduc-holdem">here</a>. 1 Adaptive (Exploitative) Approach. py. Environment Setup#. '>classic. . When your opponent is hit by your bullet, you score a point. . . We present a way to compute MaxMin strategy with the CFR algorithm. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"README. reset() while env. mpe import simple_adversary_v3 env = simple_adversary_v3. Leduc Hold'em是非完美信息博弈中最常用的基准游戏, 因为它的规模不算大, 但难度足够. A popular approach for tackling these large games is to use an abstraction technique to create a smaller game that models the original game. We have also constructed a smaller version of hold ’em, which seeks to retain the strategic ele-ments of the large game while keeping the size of the game tractable. In this tutorial, we will showcase a more advanced algorithm CFR, which uses step and step_back to traverse the game tree. Rule-based model for UNO, v1. Leduc Hold ’Em. 120 lines (98 sloc) 3. . The Analysis Panel displays the top actions of the agents and the corresponding. cfr --cfr_algorithm external --game Leduc. Rule-based model for Leduc Hold’em, v2. Search for another surname. . The goal of this thesis work is the design, implementation, and evaluation of an intelligent agent for UH Leduc Poker. 9, 3. We have shown, it is a hard task to nd global optima for Stackelberg equilibrium, even the three-player Kuhn Poker. . . This Project is based on Heinrich and Silvers Work "Neural Fictitious Self-Play in Imperfect Information Games". Rules can be found <a href="/datamllab/rlcard/blob/master/docs/games. Kuhn & Leduc Hold’em: 3-players variants Kuhn is a poker game invented in 1950 Bluffing, inducing bluffs, value betting 3-player variant used for the experiments Deck with 4 cards of the same suit K>Q>J>T Each player is dealt 1 private card Ante of 1 chip before card are dealt One betting round with 1-bet cap If there’s a outstanding bet. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with. Rule-based model for Leduc Hold’em, v2. Return type: (dict) rlcard. The experiment results demonstrate that our algorithm significantly outperforms NE baselines against non-NE opponents and keeps low exploitability at the same time. ,2015) is problematic in very large action space due to overestimating issue (Zahavy. "No-limit texas hold'em poker . . Reinforcement Learning. . Toggle navigation of MPE. Toggle navigation of MPE. Leduc Hold'em is a smaller version of Limit Texas Hold'em (first introduced in Bayes' Bluff: Opponent Modeling in Poker). Return type: (list) Leduc Poker (Southey et al) and Liar’s Dice are two different games that are more tractable than games with larger state spaces like Texas Hold'em while still being intuitive to grasp. RLCard is an open-source toolkit for reinforcement learning research in card games. . It includes the whole Game-Environment "Leduc Hold'em" which is inspired by the OpenAI Gym-Project. >> Leduc Hold'em pre-trained model >> Start a. 1 Experimental Setting. 1 Extensive Games. and three-player Leduc Hold’em poker. The deck used in UH-Leduc Hold’em, also call . Leduc No. 14 there is a diagram for a Bayes Net for Poker. We will walk through the creation of a simple Rock-Paper-Scissors environment, with example code for both AEC and Parallel environments. Leduc Hold’em; Rock Paper Scissors; Texas Hold’em No Limit; Texas Hold’em; Tic Tac Toe; MPE. . Neural network optimtzation of algorithm DeepStack for playing in Leduc Hold’em. py","path":"best. #. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"experiments","path":"experiments","contentType":"directory"},{"name":"models","path":"models. Toggle navigation of MPE. Code of conduct Activity. doudizhu-rule-v1. from rlcard. 10^23. Each game is fixed with two players, two rounds, two-bet maximum andraise amounts of 2 and 4 in the first and second round. py. Environment Setup# To follow this tutorial, you will need to install the dependencies shown below. You can also find the code in examples/run_cfr. The experiment results demonstrate that our algorithm significantly outperforms NE baselines against non-NE opponents and keeps low exploitability at the same time. md at master · Baloise-CodeCamp-2022/PokerBot-DeepStack. Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO. AI Poker Tutorial. Leduc Hold ‘em Rule agent version 1. So that good agents.