Aug 16, 2021 · The optimal policy network trained by the optimized training dataset exhibits superior performance compared to many contemporary AC algorithms ...
To improve sampling efficiency, we propose a strategy to optimize the training dataset that contains significantly less samples collected from the AC process.
The dataset optimization is made of a best episode only operation, a policy parameter-fitness model, and a genetic algorithm module. The optimal policy network ...
Optimal Actor-Critic Policy with Optimized Training Datasets ... Actor-critic (AC) algorithms are known for their efficacy and high performance in solving ...
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Aug 23, 2023 · I'm working on solving an optimisation problem using RL and currently trying out a Bounded Actor-Critic agent.
We proposed an actor-critic alignment method that allows safe offline-to-online RL and achieves strong empirical performance. To combat distribution shift, we ...
In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments.
Missing: Optimal Training
Aug 19, 2017 · PPO is a specific technique for optimizing policies which can be used in conjunction with actor-critic methods.
Missing: Optimal | Show results with:Optimal
Mar 8, 2024 · In this blog, you will learn about actor-critic methods and proximal policy optimization, two powerful techniques for reinforcement learning.
Mar 12, 2024 · In this paper, we introduce a novel Advantage-Aware Policy Optimization (A2PO) method to explicitly construct advantage-aware policy constraints for offline ...