Abstract
Reinforcement learning is an active field of machine learning that deals with developing agents that take actions in an environment with the end goal of maximizing the total reward. The field of reinforcement learning has gained increasing interest in recent years, and efforts to improve the algorithms have grown substantially. To aid in the development of better algorithms, this paper tries to evaluate the state-of-the-art reinforcement learning algorithms for solving the task of learning with raw pixels of an image as input to the algorithm by testing their performance on several benchmarks from the OpenAI Gym suite of games. This paper compares their learning capabilities and consistency throughout the multiple runs and analyzes the results of testing these algorithms to provide insights into the flaws of certain algorithms.
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References
Google Scholar (2021) Searches for reinforcement learning. https://scholar.google.com/scholar?q=%22reinforcement+learning%22&hl=en&as_sdt=0%2C5&as_ylo=2020&as_yhi=2021
Whittlestone J, Arulkumaran K, Crosby M (2021) The societal implications of deep reinforcement learning. J Artif Intell Res 70:1003–1030
Cobbe K, Klimov O, Hesse C, Kim T, Schulman J (2019) Quantifying generalization in reinforcement learning. In: International conference on machine learning, PMLR, pp 1282–1289
Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller MA (2013) Playing Atari with deep reinforcement learning, CoRR abs/1312.5602. arXiv:1312.5602. URL http://arxiv.org/abs/1312.5602
Hosu I-A, Rebedea T (2016) Playing Atari games with deep reinforcement learning and human checkpoint replay. arXiv preprint arXiv:1607.05077
Heinrich J, Silver D (2016) Deep reinforcement learning from self-play in imperfect-information games, CoRR abs/1603.01121. arXiv:1603.01121. URL http://arxiv.org/abs/1603.01121
Gamble C, Gao J (2018) Safety-first AI for autonomous data centre cooling and industrial control
Krizhevsky I, Sutskever GE (2017) Hinton, ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Schrittwieser J, Antonoglou I, Hubert T, Simonyan K, Sifre L, Schmitt S, Guez A, Lockhart E, Hassabis D, Graepel T et al (2020) Mastering Atari, go, chess and shogi by planning with a learned model. Nature 588(7839):604–609
Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. Corr abs/1707.06347. arXiv preprint arXiv:1707.06347
Duan Y, Chen X, Houthooft R, Schulman J, Abbeel P (2016) Benchmarking deep reinforcement learning for continuous control. In: International conference on machine learning, PMLR, pp 1329–1338
Whiteson S, Tanner B, Taylor ME, Stone P (2011) Protecting against evaluation overfitting in empirical reinforcement learning. In: 2011 IEEE symposium on adaptive dynamic programming and reinforcement learning (ADPRL). IEEE, pp 120–127
Machado MC, Bellemare MG, Bowling M (2017) A Laplacian framework for option discovery in reinforcement learning. In: International conference on machine learning, PMLR, pp 2295–2304
Henderson P, Islam R, Bachman P, Pineau J, Precup D, Meger D (2018) Deep reinforcement learning that matters. In: Proceedings of the AAAI conference on artificial intelligence, vol 32, pp 2–5
Dulac-Arnold G, Mankowitz D, Hester T (2019) Challenges of real-world reinforcement learning. arXiv preprint arXiv:1904.12901
Papoudakis G, Christianos F, Schäfer L, Albrecht SV (2020) Comparative evaluation of multi-agent deep reinforcement learning algorithms. arXiv preprint arXiv:2006.07869
Nichol A, Pfau V, Hesse C, Klimov O, Schulman J (2018) Gotta learn fast: a new benchmark for generalization in RL. arXiv preprint arXiv:1804.03720
Haarnoja T, Ha S, Zhou A, Tan J, Tucker G, Levine S (2018) Learning to walk via deep reinforcement learning. arXiv preprint arXiv:1812.11103
Kim J, Jeong S-H (1997) Learn to play Go, 2nd edn, vol. five volumes. Good Move Press, New York
van Hasselt H, Doron Y, Strub F, Hessel M, Sonnerat N, Modayil J (2018) Deep reinforcement learning and the deadly triad, CoRR abs/1812.02648. arXiv:1812.02648
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Sandeep Varma, N., Sinha, V., Pradyumna Rahul, K. (2023). Experimental Evaluation of Reinforcement Learning Algorithms. In: Chaki, N., Devarakonda, N., Cortesi, A. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. ICCIDE 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-99-0609-3_33
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DOI: https://doi.org/10.1007/978-981-99-0609-3_33
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