Intrinsic Reward Reinforcement Learning Agent

The agent learns intrinsic reward functions autonomously from interaction data without human intervention. The system automatically discovers what constitutes valuable states and transitions by analyzing patterns in the data, eliminating the need for hand-tuned reward parameters and enabling self-directed learning that converges faster than traditional methods.

The patent transforms the rigid, hand-crafted reward parameters into dynamic, learned parameters. Instead of fixed reward values specified by designers, the system learns reward parameters from data, allowing the reward structure to adapt and optimize automatically, thereby reducing convergence time while maintaining goal achievement reliability.

The intrinsic reward function assigns different reward values to different regions of the state space based on their informational value. Instead of uniform exploration, the agent receives higher rewards for visiting states that are locally more informative or surprising, enabling selective exploration that maximizes knowledge gain while maintaining efficiency in complex domains.

The agent performs partial exploration by focusing on the most informative aspects of the environment rather than exhaustively exploring every detail. The intrinsic reward mechanism guides the agent to sample sufficient information from critical regions while avoiding redundant exploration, achieving an optimal balance between knowledge maximization and learning efficiency.