Deep Reinforcement Learning Archive for Hard-Exploration Navigation
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Solution Overview
Problem
Conventional reinforcement learning techniques struggle with hard-exploration tasks that have sparse and deceptive reward distributions, leading to poor performance in complex real-world problems such as navigating robots, self-driving vehicles, and drones, as they fail to effectively explore and retrace promising states due to randomness and lack of resource optimization.
Innovation Solution
A deep reinforcement learning model that includes an archive to store and prioritize promising states, allowing for efficient exploration and robustification in both deterministic and noisy environments through an explore phase and a robustify phase, utilizing imitation learning to enhance model robustness and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reinforcement learning techniques take random actions to explore the environment, then the agent may discover new states, but the exploration efficiency is poor and resources are wasted on easily reachable areas
Solution Approach 1:
The patent applies preliminary action by pre-processing sensor data into discrete states and maintaining an archive of visited states before actual exploration begins. This allows the agent to systematically track and avoid re-exploring already visited states, improving exploration efficiency and reducing resource waste on redundant areas.
Solution Approach 2:
The patent replaces random mechanical exploration with a structured exploration mechanism using an archive data structure and state transition tracking. Instead of relying on random actions, the system uses deterministic state management and archive-based recall to guide exploration, substituting the mechanical random action system with an information-based guidance system.
2Productivity
If conventional techniques maximize short-term rewards, then the agent achieves higher immediate scores, but it learns wrong actions that prevent achieving higher overall scores in deceptive reward environments
Solution Approach 1:
The patent applies feedback by continuously monitoring which actions lead to archived states and using this information to adjust future exploration. The system learns from past experiences stored in the archive, providing feedback loops that prevent repeating unsuccessful patterns and guide the agent toward more reliable long-term strategies in deceptive reward environments.
Solution Approach 2:
The patent prepares the agent for deceptive reward scenarios by pre-building an archive of states and trajectories before deployment. This preliminary preparation allows the agent to recognize patterns and avoid deceptive short-term reward traps by referencing previously explored states and their long-term outcomes.
3Productivity
If the agent explores new states in noisy environments, then it may find better solutions, but the stochastic variations cause the agent to fail to retrace promising trajectories
Solution Approach 1:
The patent applies copying by maintaining an archive that stores copies of visited states and their associated trajectories. This archive serves as a reference copy that the agent can consult to retracing promising paths even when noisy environmental variations occur during actual execution, separating the ideal trajectory information from the noisy execution environment.
4Adaptability or versatility
If conventional reinforcement learning is applied to hard-exploration tasks with sparse rewards, then the agent receives minimal feedback, but this leads to poor performance and failure to solve complex real-world problems
Solution Approach 1:
The patent introduces an archive as an intermediary between the sparse reward environment and the learning agent. The archive stores and organizes state information, trajectories, and exploration history, acting as a mediator that preserves valuable information that would otherwise be lost due to sparse rewards. This intermediary structure allows the agent to recover and utilize information from past experiences even when immediate reward feedback is minimal.
Data Source
AI summary
A self-driving vehicle implements a deep reinforcement learning based model. The self-driving vehicle comprise one or more sensors configured to capture sensor data of an environment of the self-driving vehicle, a control system configured to navigate the self-driving vehicle, and a controller to determine and provide instructions to the control system. The controller implements a deep reinforcement learning based model that inputs the sensor data captured by the sensors to determine actions to perform by the control system. The model includes an archive storing states reachable by an agent in a training environment, each state stored in the archive is associated with a trajectory for reaching the state. The archive is generated by visiting states stored in the archive and performing actions to explore and find new states. New states are stored in the archive with their trajectories.