Improving Sample Efficiency in Evolutionary RL Using Off-Policy Ranking

Author:

Eshwar S. R.ORCID,Kolathaya ShishirORCID,Thoppe GuganORCID

Publisher

Springer Nature Switzerland

Reference21 articles.

1. Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: Openai gym. arXiv preprint arXiv:1606.01540 (2016)

2. Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P.: Benchmarking deep reinforcement learning for continuous control. In: International Conference on Machine Learning, pp. 1329–1338. PMLR (2016)

3. Eshwar, S., Kolathaya, S., Thoppe, G.: Improving sample efficiency in evolutionary RL using off-policy ranking arXiv:2208.10583 (2023)

4. Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: International Conference on Machine Learning, pp. 1861–1870. PMLR (2018)

5. Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., Meger, D.: Deep reinforcement learning that matters. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

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