Secured Exploration Agent for Reinforcement Learning
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Solution Overview
Problem
Conventional reinforcement learning (RL) methods are inefficient and pose safety concerns due to slow convergence, 'clumsy' exploration, and high likelihood of entering dead-end states, which can damage hardware and pose safety risks.
Innovation Solution
The development of a secured exploration agent that learns to avoid dead-end states by iteratively updating its policy to reflect the estimated probability of a state being a dead-end, thereby reducing the likelihood of transitioning to such states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reinforcement learning methods are used for training, then the agent can learn from exploration, but the training time is excessively long and the agent enters dead-end states frequently
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the set of dead-end states in a data structure (e.g., hash set) before the main reinforcement learning training begins. This allows the agent to quickly check whether a state is a dead-end state during exploration without wasting time learning from these unproductive states, thereby significantly reducing training time while maintaining learning effectiveness.
2Loss of information
If the agent explores the environment extensively, then it can gather more knowledge, but it damages hardware assets and endangers safety
Solution Approach 1:
The patent introduces an intermediary mechanism (the dead-end state detection system) that mediates between the agent's exploration behavior and the physical environment. By checking whether a state belongs to the pre-identified dead-end set before allowing the agent to explore, the system prevents the agent from entering harmful states that could damage hardware or endanger safety, while still permitting safe exploration for knowledge acquisition.
3Adaptability or versatility
If the agent spends time in dead-end trajectories, then it may discover unknown states, but it generates little relevant knowledge and exhausts resources
Solution Approach 1:
The patent extracts and removes dead-end states from the explorable state space by pre-identifying and storing them in a dedicated data structure. This extraction allows the agent to operate exclusively within the productive subset of states, eliminating wasted exploration in dead-end trajectories while maintaining adequate exploration coverage of meaningful states, thereby improving learning efficiency without sacrificing adaptability.
Data Source
AI summary
A secured exploration agent for reinforcement learning (RL) is provided. Securitizing an exploration agent includes training the exploration agent to avoid dead-end states and dead-end trajectories. During training, the exploration agent “learns” to identify and avoid dead-end states of a Markov Decision Process (MDP). The secured exploration agent is utilized to safely and efficiently explore the environment, while significantly reducing the training time, as well as the cost and safety concerns associated with conventional RL. The secured exploration agent is employed to guide the behavior of a corresponding exploitation agent. During training, a policy of the exploration agent is iteratively updated to reflect an estimated probability that a state is a dead-end state. The probability, via the exploration policy, that the exploration agent chooses an action that results in a transition to a dead-end state is reduced to reflect the estimated probability that the state is a dead-end state.