Cumulative Accessibility Estimation for Action Selection
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Solution Overview
Problem
Conventional reinforcement learning techniques, such as Q-learning, are inadequate for finding multiple paths to a goal state and balancing risks and rewards in complex environments, requiring extensive agent-environment interactions for effective results.
Innovation Solution
A cumulative accessibility estimation (CAE) system that uses a trained CAE model, potentially based on neural networks, to estimate the probability of reaching a goal state within a specified time horizon, allowing for the identification of optimal actions and reducing the need for extensive environment interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Q-learning algorithms are used to determine optimal actions, then the agent can learn from environment interactions, but the agent requires extensive interactions with the environment before effective results are achieved
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing quality values for multiple future time steps during training. These pre-computed quality values are retrieved during execution without requiring extensive real-time environment interactions, thereby reducing the time needed for the agent to learn effective action selection.
Solution Approach 2:
The system creates a copy of the environment model by pre-computing transition probability distributions and quality values offline. This copied information is then used during execution to determine actions without requiring the agent to interact extensively with the actual environment, reducing the time loss associated with learning through trial and error.
2Adaptability or versatility
If Q-learning is used to find optimal actions, then the agent learns from immediate rewards, but the agent is unable to find multiple paths to the goal state and balance relative risks and rewards
Solution Approach 1:
The system introduces dynamics by computing quality values for multiple different time steps (t=1, t=2, t=3, etc.) rather than a single fixed time step. This allows the agent to consider multiple future horizons and evaluate different paths to the goal state, enabling the agent to adapt to varying situation complexities and find optimal paths based on current context.
Solution Approach 2:
The system adds another dimension to the decision-making process by incorporating time-step variability into the quality value computation. Instead of a single static quality value, the system maintains a distribution of quality values across multiple time steps, enabling the agent to consider temporal variations and balance risks and rewards across different potential paths.
Data Source
AI summary
A cumulative accessibility estimation (CAE) system estimates the probability that an agent will reach a goal state within a time horizon to determine which actions the agent should take. The CAE system receives agent data from an agent and estimates the probability that the agent will reach a goal state within a time horizon based on the agent data. The CAE system may use a CAE model that is trained to estimate a cumulative accessibility function to estimate the probability that the agent will reach the goal state within the time horizon. The CAE system may use the CAE model to identify an optimal action for the agent based on the agent data. The CAE system may then transmit the optimal action to the agent for the agent to perform.