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Found 5 papers, 2 papers with code
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
To enhance training robustness, RL has adopted techniques from supervised learning, such as ensembles and layer normalization.
Reinforcement Learning for Fine-tuning Text-to-speech Diffusion Models
Recent advancements in generative models have sparked significant interest within the machine learning community.
Normality-Guided Distributional Reinforcement Learning for Continuous Control
Distributional reinforcement learning (DRL) has been shown to improve performance by modeling the value distribution, not just the mean.
Training Transition Policies via Distribution Matching for Complex Tasks
We introduce transition policies that smoothly connect lower-level policies by producing a distribution of states and actions that matches what is expected by the next policy.
Proximal Policy Gradient: PPO with Policy Gradient
In this paper, we propose a new algorithm PPG (Proximal Policy Gradient), which is close to both VPG (vanilla policy gradient) and PPO (proximal policy optimization).
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