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Found 5 papers, 2 papers with code
Information Content Exploration
We expand on this topic and propose a new intrinsic reward that systemically quantifies exploratory behavior and promotes state coverage by maximizing the information content of a trajectory taken by an agent.
Scope Loss for Imbalanced Classification and RL Exploration
From our analysis of the aforementioned problems we derive a novel loss function for reinforcement learning and supervised classification.
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Energy-based Surprise Minimization for Multi-Agent Value Factorization
(2) EMIX highlights a practical use of energy functions in MARL with theoretical guarantees and experiment validations of the energy operator.
Maximum Mutation Reinforcement Learning for Scalable Control
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance.
