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Found 4 papers, 2 papers with code
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations
Our approach is validated through simulations and real-world experiments, demonstrating the robot's ability to perform tasks that demand mobility and high precision, such as lifting a basket from the ground while moving, closing a dishwasher, pressing a button, and pushing a door.
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Owning to the alignment of objectives in two phases, the RL agent can transfer between offline and online learning seamlessly.
Behavior Proximal Policy Optimization
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
Solve routing problems with a residual edge-graph attention neural network
In this paper, an end-to-end deep reinforcement learning framework is proposed to solve this type of combinatorial optimization problems.
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