Multi-Agent Reward Allocation Using Counterfactual Penalties
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Solution Overview
Problem
In multi-agent reinforcement learning, agents may interfere with cooperative actions due to uneven reward distribution, leading to suboptimal learning outcomes.
Innovation Solution
A learning system that calculates evaluation values for cooperative actions with and without a target agent, using these values to determine a reward penalty or tax, ensuring appropriate reward distribution for cooperative actions among multiple agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a neural network is trained using a conventional learning system that processes all training data sequentially, then the model achieves accurate learning results, but the processing time becomes excessively long and cannot meet real-time requirements
Solution Approach 1:
The training data is divided into multiple batches that are processed in parallel across different processing units. Each batch is handled by a separate processing thread, allowing simultaneous training operations. This segmentation enables the system to process large datasets faster while maintaining learning accuracy through proper gradient aggregation across batches.
Solution Approach 2:
The system transitions from sequential single-thread processing to multi-dimensional parallel processing by utilizing multiple CPU cores or GPUs simultaneously. Each processing unit operates on different data batches in parallel, effectively adding a temporal parallelism dimension to the training process, which dramatically reduces overall processing time while preserving model convergence.
2Speed
If the learning system processes training data in parallel using multiple threads, then processing speed increases, but the system consumes excessive memory resources
Solution Approach 1:
The system extracts and reuses model parameters across different processing threads rather than duplicating entire model copies in each thread's memory. By sharing common weight matrices and only maintaining separate gradient buffers for each batch, the system reduces memory consumption while maintaining parallel processing capabilities.
Solution Approach 2:
The processing system is designed to handle multiple data batches using a unified model instance that serves all threads. The same model parameters are universally applied across all processing units, with only temporary gradient storage being thread-specific. This multi-functional approach allows parallel processing without proportionally increasing memory requirements.
3Productivity
If the learning system uses multiple processing units in parallel, then productivity increases, but the system complexity and resource management difficulty increase
Solution Approach 1:
The system merges the optimization of learning rate scheduling and gradient aggregation into a unified parallel processing framework. By combining batch normalization, gradient computation, and parameter updates into an integrated parallel execution model, the system manages complexity while maintaining high throughput through coordinated multi-unit operation.
Data Source
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AI summary
A learning system for performing reinforcement learning of a cooperative action by agents includes the agents; and a reward granting unit configured to grant a reward. The reward granting unit performs a first step of, in the presence of a target agent to which the reward is to be granted, calculating an evaluation value relating to a cooperative action of other agents as a first evaluation value; a second step of, in the absence of the target agent, calculating an evaluation value relating to a cooperative action of the other agents as a second evaluation value; and a third step of calculating a difference between the first evaluation value and the second evaluation value as a penalty of the target agent and calculating the reward to be granted to the target agent based on the penalty. The target agent performs learning of the decision-making model based on the reward granted from the reward granting unit.