WPO derives a closed-form policy update from Wasserstein gradient flows, which for Gaussian policies coincides with the standard policy gradient in expectation but with lower variance, and works for arbitrary stochastic policies without reparameterization.
CALE: Continuous Arcade Learning Environment
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abstract
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.
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cs.LG 1years
2025 1verdicts
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Wasserstein Policy Optimization
WPO derives a closed-form policy update from Wasserstein gradient flows, which for Gaussian policies coincides with the standard policy gradient in expectation but with lower variance, and works for arbitrary stochastic policies without reparameterization.