Repeated distance calculations for proximity queries can create a performance bottleneck, especially when there are many agents or resources to check. Optimizing these calculations will help improve overall system efficiency.
Proposed Solution:
Utilize Spatial Indexing Structures: Use KD-trees or spatial grids to efficiently handle proximity queries.
This can significantly reduce the computational load by only querying relevant nearby resources/agents instead of iterating through all possible objects.
Example Implementation:
from scipy.spatial import KDTree
resource_positions = [r.position for r in agent.environment.resources]
tree = KDTree(resource_positions)
# Query for resources within a certain range of the agent
indices = tree.query_ball_point(agent.position, r=agent.config.gathering_range)
nearby_resources = [agent.environment.resources[i] for i in indices]
Vectorization: Ensure all distance and other repetitive calculations are vectorized using NumPy or PyTorch where possible. This leverages hardware acceleration, improving the speed and efficiency of the code.
Benefits:
Reduces processing time for distance calculations.
Optimizes performance, especially in simulations with a large number of agents and resources.
Allows the system to scale more effectively as additional agents/resources are added.
Acceptance Criteria:
[ ] Implement KD-tree or similar spatial indexing for proximity queries.
[ ] Ensure vectorized operations for relevant calculations.
[ ] Test and confirm a measurable performance improvement with the new approach.
Additional Context:
This change will improve the agent action system, especially in dense environments with numerous resources or other agents.
Repeated distance calculations for proximity queries can create a performance bottleneck, especially when there are many agents or resources to check. Optimizing these calculations will help improve overall system efficiency.
Proposed Solution:
Utilize Spatial Indexing Structures: Use KD-trees or spatial grids to efficiently handle proximity queries.
Example Implementation:
Vectorization: Ensure all distance and other repetitive calculations are vectorized using NumPy or PyTorch where possible. This leverages hardware acceleration, improving the speed and efficiency of the code.
Benefits:
Acceptance Criteria:
Additional Context: This change will improve the agent action system, especially in dense environments with numerous resources or other agents.