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Maximum Entropy Hindsight Experience Replay

arXiv 2410.24016 ↗First posted 31 Oct 2024cs.LGcited by 0

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Abstract (arXiv)

Hindsight experience replay (HER) is well-known to accelerate goal-based reinforcement learning (RL). While HER is generally applied to off-policy RL algorithms, we previously showed that HER can also accelerate on-policy algorithms, such as proximal policy optimization (PPO), for goal-based Predator-Prey environments. Here, we show that we can improve the previous PPO-HER algorithm by selectively applying HER in a principled manner.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 31 Oct 2024.

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References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2017Hindsight Experience Replaycited by 1,136
  7. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  8. 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 176
  9. 2020Episodic Self-Imitation Learning with Hindsightcited by 10
  10. 2024Hindsight Experience Replay Accelerates Proximal Policy Optimizationcited by 2

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  2. 2026Enabling Option Learning in Sparse Rewards with Hindsight Experience Replay
  3. 2019Goal-conditioned Imitation Learning
  4. 2025Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks
  5. 2025GCHR : Goal-Conditioned Hindsight Regularization for Sample-Efficient Reinforcement Learning
  6. 2025Variance Reduction via Resampling and Experience Replay
  7. 2024Revisiting Experience Replayable Conditions
  8. 2025First-Order Representation Languages for Goal-Conditioned RL