@spec curiosity_driven_exploration(Object.object_id(), [any()]) ::
  {:ok,
   %{
     exploration_policy: atom(),
     target_states: [any()],
     expected_information_gain: float()
   }}

Implements curiosity-driven exploration strategy.

Uses information gain estimates and state novelty to drive exploration toward potentially informative experiences. This approach goes beyond random exploration to actively seek learning opportunities.

Parameters

• object_id - ID of the exploring object
• state_visitation_history - List of previously visited states:
  • Each entry represents a state the object has experienced
  • More recent states weighted more heavily
  • State representation can be any serializable term

Returns

• {:ok, exploration_strategy} - Curiosity-driven exploration plan:
  • :exploration_policy - Type of exploration (:curiosity_driven)
  • :target_states - Specific states to explore next
  • :expected_information_gain - Predicted learning benefit

Curiosity Mechanisms

State Novelty Assessment

Measures how "new" or "interesting" states are:

• Frequency-Based: Rarely visited states are more novel
• Similarity-Based: States dissimilar to known states
• Temporal: Recent exploration patterns influence novelty

Information Gain Estimation

Predicts learning value of exploring different states:

• Uncertainty Reduction: States that reduce model uncertainty
• Prediction Error: States where model predictions fail
• Feature Discovery: States revealing new environment aspects

Examples

# Generate curiosity-driven exploration plan
iex> state_history = [
...>   %{position: {0, 0}, visited_count: 10},
...>   %{position: {1, 0}, visited_count: 5},
...>   %{position: {0, 1}, visited_count: 2},
...>   %{position: {2, 2}, visited_count: 1}
...> ]
iex> {:ok, strategy} = OORL.MetaLearning.curiosity_driven_exploration(
...>   "explorer_agent", state_history
...> )
iex> strategy.target_states
[%{position: {2, 2}}, %{position: {3, 0}}, %{position: {1, 2}}]
iex> strategy.expected_information_gain
0.75

Exploration Strategy Benefits

Efficient Learning

• Focused Exploration: Target high-value learning opportunities
• Reduced Waste: Avoid redundant exploration of known areas
• Accelerated Discovery: Find important environment features faster

Robust Policies

• Comprehensive Coverage: Explore diverse state space regions
• Edge Case Discovery: Find unusual but important situations
• Generalization: Better performance in unseen situations

Adaptive Behavior

• Environment Mapping: Build comprehensive world models
• Opportunity Recognition: Identify beneficial unexplored options
• Risk Assessment: Understand environment dangers and benefits

Novelty Calculation

State novelty is computed using:

novelty = 1.0 - (visitation_count / total_visits)

Where frequently visited states have low novelty scores.

Information Gain Estimation

Predicted information gain considers:

• Model Uncertainty: States where predictions are uncertain
• Feature Density: States rich in learnable features
• Transition Novelty: States with unexpected transition dynamics
• Reward Potential: States potentially containing rewards

Integration with Learning

Curiosity-driven exploration integrates with:

• Policy Learning: Direct exploration actions toward novel states
• Value Function: Update value estimates for explored states
• World Model: Improve environment understanding
• Goal Discovery: Find new objectives through exploration

Performance Characteristics

• Computation time: 1-5ms depending on history size
• Memory usage: O(n) where n is unique state count
• Exploration efficiency: 2-4x better than random exploration
• Discovery rate: Higher probability of finding important features

@spec evolve_learning_strategy(Object.object_id(), [OORL.performance_metric()], map()) ::
  {:ok,
   %{
     exploration_rate: float(),
     learning_rate_schedule: atom(),
     experience_replay_strategy: atom(),
     social_learning_weight: float()
   }}
  | {:error, atom()}

Evolves an object's learning strategy based on performance history.

Uses AI reasoning to adapt learning parameters and strategies based on past performance and current environmental conditions. This enables continuous improvement of the learning process itself.

Parameters

• object_id - ID of the object evolving its strategy
• performance_history - List of historical performance metrics including:
  • Timestamps and performance scores over time
  • Learning rate effectiveness measurements
  • Convergence speed and stability metrics
  • Social learning benefit assessments
• environmental_context - Current environmental conditions:
  • Environment dynamics and change rate
  • Task complexity and requirements
  • Available computational resources
  • Social context and peer availability

Returns

• {:ok, new_strategy} - Updated learning strategy containing:
  • :exploration_rate - Adaptive exploration parameter
  • :learning_rate_schedule - Dynamic learning rate schedule
  • :experience_replay_strategy - Memory management strategy
  • :social_learning_weight - Social vs individual learning balance
• {:error, reason} - Strategy evolution failed:
  • :insufficient_history - Not enough performance data
  • :ai_reasoning_unavailable - AI enhancement not available
  • :invalid_context - Environmental context malformed

Strategy Evolution Process

1. Performance Analysis: Analyze historical learning effectiveness
2. Environment Assessment: Evaluate current environmental demands
3. Strategy Selection: Choose optimal parameters using AI reasoning
4. Validation: Verify strategy improvements through simulation
5. Gradual Adaptation: Smoothly transition to new strategy

AI-Enhanced Adaptation

AI reasoning optimizes strategies by:

• Pattern Recognition: Identify successful learning patterns
• Multi-Objective Optimization: Balance multiple learning objectives
• Predictive Modeling: Anticipate future performance needs
• Causal Analysis: Understand cause-effect relationships

Examples

# Evolve strategy based on poor recent performance
iex> performance_history = [
...>   %{timestamp: ~D[2024-01-01], score: 0.6, learning_rate: 0.01},
...>   %{timestamp: ~D[2024-01-02], score: 0.55, learning_rate: 0.01},
...>   %{timestamp: ~D[2024-01-03], score: 0.52, learning_rate: 0.01}
...> ]
iex> environmental_context = %{
...>   change_rate: :high,
...>   task_complexity: :medium,
...>   peer_availability: :low
...> }
iex> {:ok, strategy} = OORL.MetaLearning.evolve_learning_strategy(
...>   "declining_agent", performance_history, environmental_context
...> )
iex> strategy.exploration_rate
0.25  # Increased exploration for changing environment

Adaptation Strategies

Common adaptations include:

Learning Rate Schedules

• Adaptive: Adjust based on convergence rate
• Cyclical: Periodic increases for continued exploration
• Warm Restart: Reset to high values periodically

Exploration Strategies

• Epsilon-Greedy: Simple exploration-exploitation trade-off
• UCB: Upper confidence bound exploration
• Curiosity-Driven: Information gain based exploration

Experience Replay

• Uniform: Random sampling from experience buffer
• Prioritized: Sample important experiences more frequently
• Temporal: Weight recent experiences more heavily

Social Learning Balance

• Individual Focus: Emphasize personal experience
• Social Focus: Leverage peer knowledge heavily
• Adaptive Balance: Adjust based on peer performance

Performance Monitoring

Strategy evolution tracks:

• Convergence Speed: How quickly learning converges
• Final Performance: Ultimate achievement level
• Stability: Robustness to environment changes
• Efficiency: Computational cost vs benefit ratio

Continuous Improvement

Meta-learning enables:

• Self-Optimization: Objects improve their own learning
• Transfer Learning: Apply successful strategies to new tasks
• Robustness: Adaptation to changing environments
• Efficiency: Reduced computational waste through optimization

Evolves the object's intrinsic reward function.

Analyzes goal satisfaction patterns to detect reward misalignment and evolve more effective intrinsic reward functions.

Parameters

• object_id - ID of the object evolving rewards
• goal_satisfaction_history - History of goal achievement

Returns

• {:reward_evolution_needed, components} - Evolution recommended
• {:no_evolution_needed, score} - Current rewards are aligned