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Found 8 papers, 5 papers with code
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
Training reinforcement learning policies using environment interaction data collected from varying policies or dynamics presents a fundamental challenge.
Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
Based on this insight, we present Offline-Boosted Actor-Critic (OBAC), a model-free online RL framework that elegantly identifies the outperforming offline policy through value comparison, and uses it as an adaptive constraint to guarantee stronger policy learning performance.
Scrutinize What We Ignore: Reining Task Representation Shift In Context-Based Offline Meta Reinforcement Learning
Previous context-based approaches predominantly rely on the intuition that maximizing the mutual information between the task and the task representation ($I(Z;M)$) can lead to performance improvements.
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms.
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging.
Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic
We find that such a long-neglected phenomenon is often related to the use of inferior actions from the current policy in Bellman updates as compared to the more optimal action samples in the replay buffer.
When to Update Your Model: Constrained Model-based Reinforcement Learning
Our follow-up derived bounds reveal the relationship between model shifts and performance improvement.
Model-based Reinforcement Learning
reinforcement-learning
+1
