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Found 5 papers, 1 papers with code
Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes
Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces.
Simultaneously Updating All Persistence Values in Reinforcement Learning
In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization.
Meta Learning the Step Size in Policy Gradient Methods
Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces.
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
The choice of the control frequency of a system has a relevant impact on the ability of reinforcement learning algorithms to learn a highly performing policy.
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction
In real-world decision-making problems, for instance in the fields of finance, robotics or autonomous driving, keeping uncertainty under control is as important as maximizing expected returns.
