Introduction
Connect 4, a timeless two-player game, has intrigued both casual enthusiasts and AI researchers alike. This blog delves into using Ray RLlib and OpenSpiel to build a reinforcement learning (RL) agent for Connect 4 using self-play. Self-play enables agents to improve iteratively by competing against themselves, eliminating the need for external opponents.
What is OpenSpiel?
OpenSpiel, developed by DeepMind, is a comprehensive library of games and algorithms for RL research. It supports a wide range of games, including board games like Chess, Go, and Connect 4. With built-in tools for self-play and algorithmic experimentation, OpenSpiel is an ideal choice for developing AI agents in adversarial game environments.
Why Self-Play?
Self-play is a cornerstone of RL in competitive environments. By playing against itself, an agent continuously refines its strategies by learning from victories and defeats. Landmark achievements such as AlphaGo and AlphaZero underscore the efficacy of this approach. In Connect 4, self-play allows the agent to master complex strategies through iterative improvement.
Formalizing Self-Play
Self-play in reinforcement learning can be formalized using game theory and Markov Decision Processes (MDPs). Let’s break it down step by step:
- Game Setup: A two-player zero-sum game like Connect 4 is represented as a tuple :
- : Set of states, representing all possible configurations of the board.
- : Set of actions, corresponding to the legal moves in the game.
- : State transition probabilities when action is taken in state .
- : Reward function, defining the payoff for taking action in state .
- : Discount factor, indicating the importance of future rewards.
- Self-Play Dynamics: During training, each agent alternates between being the active player and the opponent. This dynamic ensures that the agents optimize their policies, , against a continuously improving adversary.
- Optimization Objective: The goal is to find a Nash equilibrium where neither agent can unilaterally improve its performance. Formally, this is achieved by minimizing regret:Here, is the optimal value of state , and is the value of taking action in state under the agent’s current policy.
- Learning from Outcomes: At the end of each episode, both agents update their policies based on the observed rewards and state transitions. Techniques like Q-learning or policy gradients can be employed for this purpose.
AlphaZero: The Inspiration
AlphaZero, developed by DeepMind, revolutionized the use of self-play and deep reinforcement learning in adversarial games. It builds upon the Monte Carlo Tree Search (MCTS) framework combined with neural networks to approximate policies and value functions. Here’s how AlphaZero’s core concepts relate to Connect 4 and Ray RLlib:
- Policy and Value Networks: AlphaZero uses a deep neural network to predict the policy (the probability of each action) and the value (expected outcome) for any given game state.
- Monte Carlo Tree Search (MCTS): During gameplay, AlphaZero employs MCTS to explore potential future states, balancing exploration and exploitation. The neural network guides the search, reducing computational costs compared to traditional methods.
- Self-Play: By playing against itself, AlphaZero generates high-quality data to train its neural networks. Over time, the policy improves, enabling the agent to outperform traditional approaches.
- Generalization Across Games: AlphaZero demonstrated that the same algorithm could achieve superhuman performance in Chess, Shogi, and Go, showcasing its adaptability.
Using Ray RLlib to Implement AlphaZero Principles
Ray RLlib provides a scalable and flexible framework for implementing AlphaZero-inspired algorithms. While RLlib does not directly offer a pre-built AlphaZero implementation, its components can be customized to achieve similar functionality:
- Custom Policies: RLlib allows you to define custom policy networks, enabling the integration of neural networks for approximating policies and value functions, as seen in AlphaZero.
- Self-Play with Multi-Agent Support: RLlib’s multi-agent setup simplifies the implementation of self-play. Agents can be trained concurrently, playing against themselves or each other to improve their strategies.
- Tree Search Integration: While RLlib doesn’t natively support MCTS, external libraries or custom implementations can be integrated to simulate AlphaZero’s planning approach.
- Scalability: RLlib’s distributed training capabilities allow you to scale up training across multiple nodes, enabling faster convergence for computationally intensive algorithms like AlphaZero.
Implementing Self-Play for Connect 4
Below, we'll walk through the implementation of a self-play algorithm for Connect 4 using OpenSpiel.
- Import Required Libraries
import pyspiel
import numpy as np
from open_spiel.python import rl_environment
from open_spiel.python.algorithms import tabular_qlearner- Create the Game EnvironmentWe'll create the Connect 4 environment using OpenSpiel's
rl_environmentmodule. # Create the Connect 4 game environment
game = pyspiel.load_game("connect_four")
env = rl_environment.Environment(game)
num_players = env.num_players- Set Up Agents for Self-PlayWe’ll use a simple Q-Learning agent for this example.
# Define the agents (in this case, two Tabular Q-Learners)
agents = [
tabular_qlearner.QLearner(player_id=p, num_actions=env.action_spec()['num_actions'])
for p in range(num_players)
]- Train the Agents with Self-PlayDuring training, each agent will take turns playing as Player 1 or Player 2.
num_episodes = 10000 # You can adjust this value based on the required training time
for episode in range(num_episodes):
time_step = env.reset()
while not time_step.last():
# Get actions from each player/agent
current_player = time_step.observations['current_player']
agent_output = agents[current_player].step(time_step)
action = agent_output.action
# Apply the action in the environment
time_step = env.step([action])
# Let each agent learn from the final outcome
for agent in agents:
agent.step(time_step)- Evaluating the AgentAfter training, you can evaluate the agents by having them play against each other in a match without updating the Q-values. This allows you to see how well they've learned.
eval_env = rl_environment.Environment(game)
eval_episodes = 10
for episode in range(eval_episodes):
time_step = eval_env.reset()
while not time_step.last():
current_player = time_step.observations['current_player']
action = agents[current_player].step(time_step, is_evaluation=True).action
time_step = eval_env.step([action])
print(f"Episode {episode + 1} - Winner: Player {time_step.rewards.index(max(time_step.rewards)) + 1}")
Key Concepts in Self-Play
- Exploration vs. Exploitation: Balancing exploration of new moves with exploitation of successful strategies is crucial. Techniques like ε-greedy help manage this trade-off.
- Learning from Defeats: Self-play fosters resilience by enabling agents to learn from losing games, not just victories.
- Opponent Adaptation: Agents iteratively refine strategies by exploiting weaknesses in their prior behaviors, generating robust tactics.
What’s Next?
This implementation is a foundational step. Consider expanding it by:
- Using deep Q-networks (DQN) or other neural-based approaches.
- Incorporating experience replay to optimize learning.
- Exploring policy-gradient methods or AlphaZero-inspired architectures.
Conclusion
Leveraging self-play with OpenSpiel provides a hands-on way to explore reinforcement learning in Connect 4. By iteratively improving through competition, your agents can evolve into formidable opponents. Whether you're a beginner or an experienced RL practitioner, this project offers an excellent platform to delve into adversarial game environments.
Additional Learning Materials
- Self-Play: a classic technique to train competitive agents in adversarial games
- OpenSpiel Documentation
- Ray Documentation
- AlphaZero: Shedding new light on chess, shogi, and Go
