Reinforcement Learning Device Dynamic Difficulty Adjustment
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Solution Overview
Problem
Existing reinforcement learning methods face inefficiencies in setting and updating parameters, leading to reduced learning efficiency due to inappropriate parameter settings and the inability to adjust parameters during exploration, resulting in prolonged calculation times and reduced effectiveness of acquired rewards.
Innovation Solution
A learning device and method that determine control and difficulty for a target system using observation information, calculate learning progress, and update policies based on revised evaluations, allowing for dynamic adjustment of difficulty and reward calculations to enhance learning efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning uses exploration to acquire effective rewards, then the agent can learn optimal policies, but the calculation time becomes enormous when the environment or desired behavior is complex
Solution Approach 1:
The patent changes the parameter of reward values by introducing difficulty levels. The reward value is adjusted based on the difficulty of the task, allowing the agent to receive more meaningful rewards for achieving difficult goals. This parameter transformation converts sparse, binary rewards into continuous, graded rewards that better reflect learning progress, thereby reducing the exploration time needed to achieve effective learning.
2Device complexity
If the reward function uses fixed weight coefficients for evaluation indices, then the learning calculation is simple, but it becomes difficult to set parameters appropriately for efficient reinforcement learning
Solution Approach 1:
The patent introduces dynamic difficulty levels that can be adjusted during the learning process. The difficulty parameter is not fixed but can be changed based on the agent's performance and learning progress. This dynamic adjustment allows the system to adapt the reward structure to match the agent's current capability, making the learning process more efficient without requiring complex manual parameter tuning.
Solution Approach 2:
The patent implements a feedback mechanism where the difficulty level is adjusted based on the agent's performance. When the agent achieves certain milestones or demonstrates improved performance, the difficulty level is increased, which in turn adjusts the reward values. This feedback loop automatically tunes the parameter settings based on learning progress, eliminating the need for manual parameter adjustment while maintaining learning efficiency.
3Adaptability or versatility
If reinforcement learning requires a huge amount of exploration to acquire effective rewards, then the agent can learn from diverse experiences, but the calculation time to acquire the desired policy becomes enormous
Solution Approach 1:
The patent transforms the reward parameter structure by introducing difficulty-based scaling. Instead of using fixed, sparse rewards, the system dynamically scales reward values according to the difficulty of achieved goals. This parameter transformation makes rewards more informative and dense, allowing the agent to learn effectively from fewer explorations rather than requiring exhaustive search through the state space.
Data Source
AI summary
The learning device 800 includes a determination unit 801 determining control to be applied to the target system and difficulty to be set to the target system using observation information regarding the target system and difficulty corresponding to a way of state transition of the target system and how likely it is to be rated highly related to the contents of the control, according to the policy, a learning progress calculation unit 802 calculating learning progress of the policy using a plurality of original evaluations of states before and after transition of the target system and the determined control, according to the determined control and the determined difficulty, a calculation unit 803 calculating revised evaluation using the original evaluation, the determined difficulty, and the calculated learning progress, and a policy updating unit 804 updating the policy using the observation information, the determined control, the determined difficulty, and the revised evaluation.