Multi-Goal Reinforcement Learning
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Benchmarks
no extra data
Most implemented
Proximal Policy Optimization Algorithms
Playing Atari with Deep Reinforcement Learning
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Papers
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks
Hindsight Experience Replay (HER) is widely regarded as the state-of-the-art algorithm for achieving sample-efficient multi-goal reinforcement learning (RL) in robotic manipulation tasks with binary rewards. HER facilita…
Multi-Goal Reinforcement LearningReinforcement Learning (RL)Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
Humanoid robots must master numerous tasks with sparse rewards, posing a challenge for reinforcement learning (RL). We propose a method combining RL and automated planning to address this. Our approach uses short goal-co…
Multi-Goal Reinforcement LearningReinforcement Learning (RL)Solving Multi-Goal Robotic Tasks with Decision Transformer
Artificial intelligence plays a crucial role in robotics, with reinforcement learning (RL) emerging as one of the most promising approaches for robot control. However, several key challenges hinder its broader applicatio…
Multi-Goal Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)FDQN: A Flexible Deep Q-Network Framework for Game Automation
In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as w…
Atari GamesDecision MakingMulti-Goal Reinforcement LearningMRHER: 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 ManipulationUnderstanding Hindsight Goal Relabeling from a Divergence Minimization Perspective
Hindsight goal relabeling has become a foundational technique in multi-goal reinforcement learning (RL). The essential idea is that any trajectory can be seen as a sub-optimal demonstration for reaching its final state. …
Imitation LearningMulti-Goal Reinforcement LearningQ-LearningReinforcement Learning (RL)