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Found 10 papers, 4 papers with code
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning
Applying diffusion models in reinforcement learning for long-term planning has gained much attention recently.
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
In offline reinforcement learning (RL), the performance of the learned policy highly depends on the quality of offline datasets.
Diffusion Models for Reinforcement Learning: A Survey
Diffusion models surpass previous generative models in sample quality and training stability.
MADiff: Offline Multi-agent Learning with Diffusion Models
To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller.
Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents
In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their developers or physical restrictions.
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow
High-quality traffic flow generation is the core module in building simulators for autonomous driving.
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption.
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization
State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
