GitHub - ImanRHT/QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge ComputingThe QECO algorithm optimizes resource utilization in Mobile Edge Computing by balancing QoE factors through advanced reinforcement learning techniques.
This Deep-learning Approach Can Help Double Your Gains in Crypto Investments | HackerNoonThe article highlights a novel DRL agent using Transformers for improved cryptocurrency trading adaptability and profitability.
Evaluating Deep RL Agents in Hedging with Market-Calibrated Stochastic Volatility Models | HackerNoonDRL agents outperform BS Delta strategies in hedging performance, particularly under transaction costs.
This Deep-learning Approach Can Help Double Your Gains in Crypto Investments | HackerNoonThe article highlights a novel DRL agent using Transformers for improved cryptocurrency trading adaptability and profitability.
Evaluating Deep RL Agents in Hedging with Market-Calibrated Stochastic Volatility Models | HackerNoonDRL agents outperform BS Delta strategies in hedging performance, particularly under transaction costs.
Hedging American Put Options with Deep Reinforcement Learning: Training Procedures | HackerNoonRobust asset data generation and reward structure are essential for training DRL agents in hedging American put options.
Advancements in Deep Reinforcement Learning for Hedging American Put Options | HackerNoonThe article introduces DRL agents for hedging American put options, enhancing existing methodologies in the field.
Results of Deep Reinforcement Learning Agent Performance in Hedging American Put Options | HackerNoonThe effectiveness of a DRL agent depends significantly on transaction costs and market conditions.
Hedging American Put Options with Deep Reinforcement Learning: Training Procedures | HackerNoonRobust asset data generation and reward structure are essential for training DRL agents in hedging American put options.
Advancements in Deep Reinforcement Learning for Hedging American Put Options | HackerNoonThe article introduces DRL agents for hedging American put options, enhancing existing methodologies in the field.
Results of Deep Reinforcement Learning Agent Performance in Hedging American Put Options | HackerNoonThe effectiveness of a DRL agent depends significantly on transaction costs and market conditions.
Addendum: A graph placement methodology for fast chip design - NatureAlphaChip represents a significant advancement in AI for engineering, offering enhanced chip layout generation through deep reinforcement learning.
Generative AI that imitates human motionResearchers have developed a method combining central pattern generators and deep reinforcement learning for robots to imitate human walking and running motions effectively.
Trotting robots reveal emergence of animal gait transitionsEPFL robot learns pronking via DRL to navigate gaps, shedding light on gait transitions due to fall avoidance, not just energy efficiency.