arXiv:2104.04326
공식 코드 · Papers with Code
CropGym: a Reinforcement Learning Environment for Crop Management
원문 초록
Nitrogen fertilizers have a detrimental effect on the environment, which can be reduced by optimizing fertilizer management strategies. We implement an OpenAI Gym environment where a reinforcement learning agent can learn fertilization management policies using process-based crop growth models and identify policies with reduced environmental impact. In our environment, an agent trained with the Proximal Policy Optimization algorithm is more successful at reducing environmental impacts than the other baseline agents we present.
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Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Exploring the robustness of TractOracle methods in RL-based tractography
Deep Reinforcement Learning with Gradient Eligibility Traces
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
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