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projects/zero-shot-rl/index.html

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 [Paper] [Code] [Poster] [Slides]
  Summary Zero-shot reinforcement learning (RL) methods learn general policies that can, in principle, solve any unseen task in an environment. Recently, methods leveraging successor features and successor measures have emerged as viable zero-shot RL candidates, returning near-optimal policies for many unseen tasks. However, to enable this, they have assumed access to unrealistically large and heterogeneous datasets of transitions for pre-training."><meta itemprop="datePublished" content="2023-09-26T17:27:21+01:00" />
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+<meta itemprop="wordCount" content="421"><meta itemprop="image" content="https://enjeeneer.io"/>
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@@ -219,7 +219,7 @@ <h2 id="intuition">Intuition</h2>
 <p><small><em>Figure 2: <strong>Conservative zero-shot RL methods</strong>. VC-FB (right) suppresses the predicted values for OOD state-action pairs**</em></small></p>
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 <h2 id="results">Results</h2>
-<p>We demonstrate our methods improve performance w.r.t standard zero-shot RL / GCRL baselines on low quality datasets from ExORL (Figure 4) and D4RL (Figure 5)   .
+<p>We demonstrate our methods improve performance w.r.t standard zero-shot RL / GCRL baselines on low quality datasets from ExORL (Figure 3) and D4RL (Figure 4), and do not hinder performance on large and diverse datasets (Figure 5).
 <figure><img src="https://github.com/enjeeneer/zero-shot-rl/blob/main/media/performance-profiles-subplot2.png?raw=true"/>
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