CALE: Continuous Arcade Learning Environment

arXiv 2410.23810 ↗First posted 31 Oct 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such as PPO [Schulman et al., 2017] and SAC [Haarnoja et al., 2018]) and value-based agents (such as DQN [Mnih et al., 2015] and Rainbow [Hessel et al., 2018]) on the same environment suite. We provide a series of open questions and research directions that CALE enables, as well as initial baseline results using Soft Actor-Critic. CALE is available as part of the ALE athttps://github.com/Farama-Foundation/Arcade-Learning-Environment.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 44 of its 71 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

44 of 71 references.

References · 44

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2020Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixelscited by 585

32 more

2019Model-Based Reinforcement Learning for Ataricited by 502
2020Learning Invariant Representations for Reinforcement Learning without Reconstructioncited by 436
2020dm_control: Software and Tasks for Continuous Controlcited by 375
2020Data-Efficient Reinforcement Learning with Self-Predictive Representationscited by 318
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2017Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agentscited by 269
2022The Primacy Bias in Deep Reinforcement Learningcited by 247
2019Soft Actor-Critic for Discrete Action Settingscited by 174
2023The Dormant Neuron Phenomenon in Deep Reinforcement Learningcited by 174
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2024Mixtures of Experts Unlock Parameter Scaling for Deep RLcited by 78
2019Discretizing Continuous Action Space for On-Policy Optimizationcited by 68
2022The State of Sparse Training in Deep Reinforcement Learningcited by 61
2021The Difficulty of Passive Learning in Deep Reinforcement Learningcited by 50
2021MICo: Improved representations via sampling-based state similarity for Markov decision processescited by 43
2023Proto-Value Networks: Scaling Representation Learning with Auxiliary Taskscited by 42
2024In value-based deep reinforcement learning, a pruned network is a good networkcited by 39
2021On Bonus-Based Exploration Methods in the Arcade Learning Environmentcited by 37
2021DACBench: A Benchmark Library for Dynamic Algorithm Configurationcited by 28
2022The Phenomenon of Policy Churncited by 28
2018Reinforcement Learning Testbed for Power-Consumption Optimizationcited by 18
2024Do Transformer World Models Give Better Policy Gradients?cited by 10
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2022SMPL: Simulated Industrial Manufacturing and Process Control Learning Environmentscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

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