PEARL: Probabilistic Embeddings for Actor-Critic RL

Author(s): Sherwin Chen

Originally published on Towards AI.

A sample-efficient meta reinforcement learning method

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Meta reinforcement learning could be particularly challenging because the agent has to not only adapt to the new incoming data but also find an efficient way to explore the new environment. Current meta-RL algorithms rely heavily on on-policy experience, which limits their sample efficiency. Worse still, most of them lack mechanisms to reason about task uncertainty when adapting to a new task, limiting their effectiveness in sparse reward problems.

We discuss a meta-RL algorithm that attempts to address these challenges. In a nutshell, the algorithm, namely Probabilistic Embeddings for Actor-Critic RL(PEARL) proposed by Rakelly & Zhou et al. in… Read the full blog for free on Medium.

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Published via Towards AI