Advanced Training Techniques

This guide covers sophisticated training approaches for developing high-performance AI agents in Casino of Life.

Curriculum Learning

Curriculum learning involves training an agent on progressively more difficult tasks, similar to how humans learn.

Implementation

from casino_of_life.training import CurriculumTrainer
from casino_of_life.agents import DynamicAgent

# Define curriculum stages
curriculum = [
    {
        "name": "basics",
        "opponent_difficulty": "very_easy",
        "timesteps": 50000,
        "success_metric": "win_rate",
        "success_threshold": 0.7
    },
    {
        "name": "intermediate",
        "opponent_difficulty": "medium",
        "timesteps": 100000,
        "success_metric": "win_rate",
        "success_threshold": 0.5
    },
    {
        "name": "advanced",
        "opponent_difficulty": "hard",
        "timesteps": 150000,
        "success_metric": "win_rate",
        "success_threshold": 0.4
    }
]

# Create curriculum trainer
trainer = CurriculumTrainer(
    agent=DynamicAgent(env),
    curriculum=curriculum,
    evaluation_frequency=5000
)

# Start curriculum training
trainer.train()

Benefits

• More stable learning progression

• Better final performance

• Reduced training time

• Avoids getting stuck in local optima

Imitation Learning

Jumpstart your agent's performance by learning from human demonstrations or expert agents.

Implementation

from casino_of_life.training import ImitationLearner
from casino_of_life.data import DemonstrationLoader

# Load demonstration data
demo_loader = DemonstrationLoader()
demos = demo_loader.load("expert_liu_kang_demos.pkl")

# Create imitation learner
imitator = ImitationLearner(
    env=env,
    demonstrations=demos,
    learning_rate=0.001,
    batch_size=64
)

# Train on demonstrations
imitator.train(epochs=10)

# Create agent with pre-trained policy from imitation
agent = DynamicAgent(
    env=env,
    initial_policy=imitator.get_policy()
)

# Fine-tune with reinforcement learning
agent.train(timesteps=100000)

Creating Demonstration Data

Record your own gameplay for imitation learning:

from casino_of_life.data import DemonstrationRecorder

# Start recording gameplay
recorder = DemonstrationRecorder(env)
recorder.start_recording()

# Play the game (controls will be recorded)
# ...

# Stop recording and save demonstrations
recorder.stop_recording()
recorder.save("my_liu_kang_demos.pkl")

Multi-Agent Training

Train your agent against other learning agents for more robust strategies.

Implementation

from casino_of_life.training import MultiAgentTrainer
from casino_of_life.agents import DynamicAgent

# Create multiple agents
agent1 = DynamicAgent(env, name="aggressive_agent")
agent2 = DynamicAgent(env, name="defensive_agent")
agent3 = DynamicAgent(env, name="balanced_agent")

# Create multi-agent trainer
trainer = MultiAgentTrainer(
    agents=[agent1, agent2, agent3],
    match_making="round_robin",
    matches_per_iteration=10,
    total_iterations=100
)

# Start multi-agent training
trainer.train()

Tournament Evaluation

Evaluate multiple trained agents in a tournament setting:

from casino_of_life.evaluation import TournamentEvaluator

evaluator = TournamentEvaluator(
    agents=[agent1, agent2, agent3],
    matches_per_pair=20,
    evaluation_metrics=["win_rate", "damage_efficiency", "combo_frequency"]
)

results = evaluator.run_tournament()
evaluator.display_results()

Hierarchical Reinforcement Learning

Implement hierarchical policies for complex behavior patterns.

Implementation

from casino_of_life.agents import HierarchicalAgent
from casino_of_life.policies import HighLevelPolicy, LowLevelPolicy

# Create high-level policy (strategy selection)
high_level_policy = HighLevelPolicy(
    strategies=["aggressive", "defensive", "neutral", "counter"],
    selection_frequency=30  # frames between strategy changes
)

# Create low-level policies (action execution)
low_level_policies = {
    "aggressive": LowLevelPolicy(focus="attack"),
    "defensive": LowLevelPolicy(focus="block"),
    "neutral": LowLevelPolicy(focus="positioning"),
    "counter": LowLevelPolicy(focus="counter_attack")
}

# Create hierarchical agent
agent = HierarchicalAgent(
    env=env,
    high_level_policy=high_level_policy,
    low_level_policies=low_level_policies
)

# Train both levels simultaneously
agent.train(timesteps=200000)

Self-Play with Progressive Sampling

Use self-play with progressive opponent sampling to create increasingly skilled agents.

Implementation

from casino_of_life.training import SelfPlayTrainer

trainer = SelfPlayTrainer(
    env=env,
    initial_agent=DynamicAgent(env),
    checkpoint_frequency=10000,  # Save model every 10k steps
    opponent_sampling={
        "latest_model_probability": 0.7,
        "random_historical_probability": 0.2,
        "initial_model_probability": 0.1
    },
    total_timesteps=500000
)

trainer.train()

Hybrid Training Approaches

Combine multiple training techniques for optimal results.

Implementation

from casino_of_life.training import HybridTrainer

# Configure hybrid training pipeline
trainer = HybridTrainer(
    env=env,
    agent=DynamicAgent(env),
    pipeline=[
        {"type": "imitation", "epochs": 5, "demo_file": "expert_demos.pkl"},
        {"type": "curriculum", "curriculum": basic_curriculum},
        {"type": "self_play", "timesteps": 100000},
        {"type": "multi_agent", "opponents": [agent1, agent2], "timesteps": 50000}
    ]
)

# Execute the full training pipeline
trainer.train()

Meta-Learning for Character Adaptation

Train agents that can quickly adapt to different fighting game characters.

Implementation

from casino_of_life.training import MetaLearner
from casino_of_life.environment import CharacterTask

# Define character-specific tasks
character_tasks = [
    CharacterTask(character="LiuKang", episodes=10),
    CharacterTask(character="Scorpion", episodes=10),
    CharacterTask(character="SubZero", episodes=10),
    # Add more characters
]

# Create meta-learner
meta_learner = MetaLearner(
    env=env,
    character_tasks=character_tasks,
    meta_batch_size=5,
    inner_learning_rate=0.01,
    outer_learning_rate=0.001,
    adaptation_steps=5
)

# Train meta-learning agent
meta_learner.train(meta_iterations=1000)

# Test rapid adaptation to new character
adapted_agent = meta_learner.adapt(character="Reptile", adaptation_episodes=5)

Ensemble Methods

Combine multiple policies for more robust decision-making.

Implementation

from casino_of_life.agents import EnsembleAgent
from casino_of_life.policies import PolicyEnsemble

# Create individual specialized policies
fireball_policy = train_specialized_policy(move="fireball")
uppercut_policy = train_specialized_policy(move="uppercut")
defensive_policy = train_specialized_policy(focus="defense")

# Create policy ensemble
ensemble = PolicyEnsemble(
    policies=[fireball_policy, uppercut_policy, defensive_policy],
    voting_method="weighted",
    weights=[0.4, 0.4, 0.2]
)

# Create ensemble agent
agent = EnsembleAgent(
    env=env,
    policy_ensemble=ensemble,
    state_analyzer=StateAnalyzer()  # Analyzes game state to inform policy selection
)

# Fine-tune ensemble weights
agent.optimize_weights(evaluation_episodes=100)

Evolutionary Strategies

Use evolutionary algorithms to optimize agent hyperparameters and network architectures.

Implementation

from casino_of_life.optimization import EvolutionaryOptimizer
from casino_of_life.agents import DynamicAgent

# Define parameter space to explore
parameter_space = {
    "learning_rate": [0.0001, 0.0005, 0.001, 0.005],
    "network_width": [64, 128, 256],
    "network_depth": [2, 3, 4],
    "activation": ["relu", "tanh", "elu"],
    "frame_stack": [2, 4, 6]
}

# Create evolutionary optimizer
optimizer = EvolutionaryOptimizer(
    env=env,
    agent_class=DynamicAgent,
    parameter_space=parameter_space,
    population_size=20,
    generations=30,
    evaluation_episodes=10,
    fitness_metric="win_rate"
)

# Run optimization
best_params, best_agent = optimizer.optimize()

Continual Learning

Implement mechanisms to prevent catastrophic forgetting when learning new skills.

Implementation

from casino_of_life.training import ContinualLearner
from casino_of_life.memory import ExperienceReplay

# Create experience replay buffer
replay_buffer = ExperienceReplay(capacity=100000)

# Create continual learner
learner = ContinualLearner(
    env=env,
    experience_replay=replay_buffer,
    regularization_method="elastic_weight_consolidation",
    importance_sampling=True,
    replay_ratio=0.3  # Ratio of old experiences to new experiences
)

# Learn sequence of tasks while preserving earlier knowledge
learner.learn_task("basic_movement", timesteps=50000)
learner.learn_task("special_moves", timesteps=50000)
learner.learn_task("combos", timesteps=50000)
learner.learn_task("counter_strategies", timesteps=50000)

Best Practices for Advanced Training

1. Combine approaches: The most effective agents typically use multiple training techniques

2. Monitor carefully: Use the web interface to closely track key metrics during advanced training

3. Progressive complexity: Start with simpler approaches before advancing to more complex methods

4. Regular evaluation: Frequently evaluate your agent against baseline models

5. Resource management: More advanced techniques often require more computational resources

6. Version control: Keep track of all model versions and their performance

7. Ablation studies: Test which components contribute most to your agent's performance

By leveraging these advanced training techniques, you can create sophisticated fighting game AI agents with nuanced, adaptive behaviors that can compete at high levels of play.