Potential-Based Reward Shaping is an advanced technique within reinforcement learning that introduces auxiliary reward signals, derived from a potential function, to guide an agent’s learning process. This method accelerates convergence to an optimal policy without altering the fundamental optimality of the original reward function, thereby preserving the true objective while improving learning efficiency.
Mechanism
The core mechanism involves defining a potential function that assigns a scalar value to each state of the environment. The change in this potential function between successive states is then added to the intrinsic reward received from the environment. This provides immediate, informative feedback for intermediate actions that move the agent closer to desired outcomes, even if those steps do not yield direct environmental rewards.
Methodology
In the context of smart trading or decentralized finance (DeFi) protocol optimization, this methodology is applied to train intelligent agents to navigate complex, multi-step transaction sequences or resource allocation problems. It involves engineering a potential function that reflects desirable economic states or efficient pathing through a blockchain network. This technique guides agents to execute profitable trades, manage liquidity, or participate in governance more effectively, improving system performance.
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