Added #

• Flutter SDK dependency for seamless Flutter integration
• collection package (^1.17.0) for enhanced data structures
• equatable package (^2.0.5) for value equality in state and action classes
• AgentNotifier class: ChangeNotifier wrapper for Flutter state management integration
• trainStream extension method for reactive UI updates with stream-based training
• Decay schedules: LinearDecaySchedule and ExponentialDecaySchedule for epsilon decay
• TrainingStats class for tracking episode-level training metrics
• AggregatedStats class for computing statistics across multiple episodes
• QTableSerializer for saving and loading trained Q-tables to/from disk
• Comprehensive Flutter integration documentation in README with examples
• Complete Flutter demo app in example/flutter_rl_demo/ with real-time visualization
• Examples for both stream-based training and ChangeNotifier pattern
• Documentation on decay schedules, training statistics, and model persistence

Features #

• Real-time training visualization for Flutter applications
• Stream-based training with reactive UI updates via trainStream
• Flutter state management integration through AgentNotifier (ChangeNotifier pattern)
• Compatible with Provider, Riverpod, and other Flutter state management solutions
• Configurable epsilon decay schedules (linear and exponential)
• Training statistics tracking with episode-level and aggregated metrics
• Q-table serialization for saving and loading trained agents
• Interactive Flutter example demonstrating real-time RL training visualization
• Support for training progress monitoring with episode, reward, steps, and epsilon tracking
• Non-blocking asynchronous training for smooth UI performance

Changed #

• Refactored State to DartRLState for improved naming consistency
• Refactored Action to DartRLAction for improved naming consistency
• Refactored StateAction to DartRLStateAction for improved naming consistency

Added #

• Initial alpha release of dart_rl package
• Q-Learning algorithm implementation (QLearningAgent)
• SARSA algorithm implementation (SarsaAgent)
• Expected-SARSA algorithm implementation (ExpectedSarsaAgent)
• Environment interface for creating custom RL environments
• Agent base class with epsilon-greedy exploration strategy
• State, Action, and StateAction classes for representing RL components
• StepResult class for environment step results
• Grid World example environment
• Frozen Lake example environment
• Comprehensive unit tests
• Documentation and README with usage examples

Features #

• Support for discrete state and action spaces
• Configurable learning rate (α), discount factor (γ), and epsilon (ε)
• Epsilon-greedy exploration with decay functionality
• Q-table access for inspection and debugging
• Training methods for single episodes and multiple episodes
• Compatible with both Dart and Flutter applications

Added #

• Initial release of dart_rl package
• Q-Learning algorithm implementation (QLearningAgent)
• SARSA algorithm implementation (SarsaAgent)
• Expected-SARSA algorithm implementation (ExpectedSarsaAgent)
• Environment interface for creating custom RL environments
• Agent base class with epsilon-greedy exploration strategy
• State, Action, and StateAction classes for representing RL components
• StepResult class for environment step results
• Grid World example environment
• Frozen Lake example environment
• Comprehensive unit tests
• Documentation and README with usage examples

Features #

• Support for discrete state and action spaces
• Configurable learning rate (?), discount factor (?), and epsilon (?)
• Epsilon-greedy exploration with decay functionality
• Q-table access for inspection and debugging
• Training methods for single episodes and multiple episodes
• Compatible with both Dart and Flutter applications