Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
Not on the map. Why?
Abstract (arXiv)
Learning in high dimensional continuous tasks is challenging, mainly when the experience replay memory is very limited. We introduce a simple yet effective experience sharing mechanism for deterministic policies in continuous action domains for the future off-policy deep reinforcement learning applications in which the allocated memory for the experience replay buffer is limited. To overcome the extrapolation error induced by learning from other agents' experiences, we facilitate our algorithm with a novel off-policy correction technique without any action probability estimates. We test the effectiveness of our method in challenging OpenAI Gym continuous control tasks and conclude that it can achieve a safe experience sharing across multiple agents and exhibits a robust performance when the replay memory is strictly limited.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 27 Jul 2022.
Builds on
11 of 20 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2016OpenAI Gymcited by 1,736
- 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
- 2017Deep Reinforcement Learning that Matterscited by 754
- 2016Safe and Efficient Off-Policy Reinforcement Learningcited by 264
- 2019Off-Policy Actor-Critic with Shared Experience Replaycited by 39
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.