Value-Based Action Selection in Reinforcement Learning
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Solution Overview
Problem
Conventional reinforcement learning algorithms face challenges in efficiently selecting actions in large action and state spaces, leading to slow convergence and sub-optimal performance due to redundant exploration strategies.
Innovation Solution
A value-based action selection strategy is introduced, which evaluates the consequences of previous actions and selects subsequent actions based on the angle or dot product between candidate actions and previous actions, reducing the action space and improving convergence speed by focusing on actions with positive or negative consequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reinforcement learning algorithms use random exploration strategies in large action and state spaces, then the agent can discover potential actions, but the convergence speed becomes slow and performance becomes sub-optimal due to redundant exploration
Solution Approach 1:
The patent changes the parameter of action selection from uniform random selection to value-based selection. By using Q-values or estimated values to guide action selection, the algorithm prioritizes actions with higher expected returns, transforming the exploration strategy from blind random selection to informed selection based on learned value parameters.
Solution Approach 2:
The patent applies preliminary action by pre-evaluating candidate actions using their estimated values before actual execution. The value-based action selection mechanism pre-ranks actions based on their expected outcomes, so that when exploration is needed, the agent explores from a pre-filtered set of promising actions rather than the entire action space.
2Adaptability or versatility
If the agent explores all actions in large action spaces uniformly, then comprehensive coverage is achieved, but the complexity of the exploration process increases and efficiency decreases
Solution Approach 1:
The patent introduces value parameters (Q-values, estimated returns) to characterize and differentiate actions. By changing from uniform selection to value-based selection, the algorithm maintains adaptability to the full action space while reducing complexity through parameter-driven prioritization of promising actions.
Solution Approach 2:
The patent segments the action space into priority levels based on action values. Instead of treating all actions uniformly, the algorithm divides actions into high-value and low-value categories, allowing focused exploration of high-value regions while still maintaining the ability to access the entire action space when needed.
3Reliability
If reinforcement learning uses traditional exploration strategies, then the agent can learn from all possible actions, but the number of trials required to achieve optimal performance increases significantly
Solution Approach 1:
The patent changes the exploration parameter from uniform probability distribution to value-based probability distribution. Actions with higher estimated values receive higher selection probabilities, ensuring that learning trials are distributed proportionally to their expected importance, thus maintaining learning completeness while improving efficiency.
Solution Approach 2:
The patent implements feedback by using learned action values to guide subsequent action selections. The Q-values or estimated returns, which are updated through learning, provide feedback signals that shape the exploration policy, creating a closed-loop system where learning outcomes directly influence future exploration behavior.
Data Source
AI summary
A method and agent for reinforcement learning. The method may include evaluating a consequence of a previous action. Evaluating the consequence may include performing a comparison of one or more current monitored parameters (e.g., immediate reward, accumulated reward, average reward, and/or current key performance parameters) to one or more previous monitored parameters. The method may include, based on the evaluated consequence of the previous action, determining a subset of potential next actions. For a positive consequence, the determined subset of potential next actions may include only potential next actions that are likely to have the consequence as the previous action (e.g., based on a dot product of or angle between vectors of the previous action and the potential next action). The method may include selecting an action from the determined subset of potential next actions. The method may include performing the selected action.