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learn reinforcement learning
markov decision processes, q-learning, policy gradients, and deep rl in practice.
curatedmixed~5 weeks, part-time
reinforcement learning, practically
from the bellman equation to deep rl agents you can train in an afternoon — theory paired with clean, runnable implementations.
4 modules · 12 resources · checkpoint per modulestay current
see the full digest →what's new in reinforcement learning
- Reinforcement learning-based decision support for admission-time inpatient bed allocationthis paper applies reinforcement learning to optimize the complex task of allocating inpatient beds when patients are admitted. for practitioners in hospital administration, this offers a data-driven decision support system to improve patient flow and resource utilization in real-time.
- Optimizing one-dimensional bin packing for conveyor belt logistics via pointer-network-based A2C and heuristicsthis paper applies reinforcement learning, specifically a2c with pointer networks, to optimize one-dimensional bin packing for conveyor belts. for practitioners in logistics and manufacturing, this means potentially more efficient use of space and faster throughput in automated systems, reducing operational costs.
- Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulumthis paper proposes using the lyapunov characteristic exponent as a dense reward signal for reinforcement learning to discover stabilization policies. for practitioners working on control systems, this offers a novel way to design reward functions that guide agents towards stable and robust behaviors.
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