Papers FetchPush-v1
“FetchPush-v1” 태그가 달린 논문 5편 · 필터 해제
MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse Rewards
Sparse rewards pose a significant challenge to achieving high sample efficiency in goal-conditioned reinforcement learning (RL). Specifically, in sequential manipulation tasks, the agent receives failure rewards until it…
FetchPush-v1Multi-Goal Reinforcement LearningReinforcement Learning (RL)Robot ManipulationAACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay
Actor learning and critic learning are two components of the outstanding and mostly used Deep Deterministic Policy Gradient (DDPG) reinforcement learning method. Since actor and critic learning plays a significant role i…
Deep Reinforcement LearningFetchPush-v1reinforcement-learningReinforcement Learning (RL)Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards
Exploration with sparse rewards remains a challenging research problem in reinforcement learning (RL). Especially for sequential object manipulation tasks, the RL agent always receives negative rewards until completing a…
FetchPush-v1Reinforcement Learning (RL)Robot ManipulationImaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts. However, current state-of-the-art model-based met…
FetchPush-v1Model-based Reinforcement LearningOpenAI GymACDER: Augmented Curiosity-Driven Experience Replay
Exploration in environments with sparse feedback remains a challenging research problem in reinforcement learning (RL). When the RL agent explores the environment randomly, it results in low exploration efficiency, espec…
FetchPush-v1Reinforcement Learning (RL)