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Mattia Rigotti<p>This <a href="https://mastodon.social/tags/DeepRL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepRL</span></a> paper from University of Alberta seems quite cool:</p><p>"Deep reinforcement learning without experience replay, target networks, or batch updates"</p><p>As the title says, they succeeded in training deep RL networks in streaming setting getting rid of replay buffers.<br>The main tricks for that to work seem to be signal normalization and bounding the step-size 🤯</p><p>💻Code: <a href="http://github.com/mohmdelsayed/streaming-drl" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">github.com/mohmdelsayed/stream</span><span class="invisible">ing-drl</span></a><br>📄Paper: <a href="https://openreview.net/pdf?id=yqQJGTDGXN" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">openreview.net/pdf?id=yqQJGTDG</span><span class="invisible">XN</span></a></p><p><a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/RL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RL</span></a> <a href="https://mastodon.social/tags/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a></p>
Kalhan<p>So here&#39;s my introduction: <br />I am a Data Science MSc student at Nottingham Trent University. Mainly here to get updates on <a href="https://sigmoid.social/tags/ReinforcementLearning" class="mention hashtag" rel="tag">#<span>ReinforcementLearning</span></a> research and connect with people who has an interest in <a href="https://sigmoid.social/tags/RL" class="mention hashtag" rel="tag">#<span>RL</span></a>.</p><p><a href="https://sigmoid.social/tags/reinforcementlearning" class="mention hashtag" rel="tag">#<span>reinforcementlearning</span></a> <a href="https://sigmoid.social/tags/reinforcement_learning" class="mention hashtag" rel="tag">#<span>reinforcement_learning</span></a> <a href="https://sigmoid.social/tags/RL" class="mention hashtag" rel="tag">#<span>RL</span></a> <a href="https://sigmoid.social/tags/MARL" class="mention hashtag" rel="tag">#<span>MARL</span></a> <a href="https://sigmoid.social/tags/DeepRL" class="mention hashtag" rel="tag">#<span>DeepRL</span></a></p>
Marc Lanctot<p>Today is the <a href="https://sigmoid.social/tags/NeurIPS2022" class="mention hashtag" rel="tag">#<span>NeurIPS2022</span></a> <a href="https://sigmoid.social/tags/DeepRL" class="mention hashtag" rel="tag">#<span>DeepRL</span></a> workshop, starting in just a few hours!</p><p>We have two papers there which may be of interest to <a href="https://sigmoid.social/tags/MARL" class="mention hashtag" rel="tag">#<span>MARL</span></a> <a href="https://sigmoid.social/tags/gametheory" class="mention hashtag" rel="tag">#<span>gametheory</span></a> <a href="https://sigmoid.social/tags/RL" class="mention hashtag" rel="tag">#<span>RL</span></a> researchers: </p><p>- ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret my McAleer et al. (<a href="https://openreview.net/forum?id=GMMdlnRYj4" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=GMMdln</span><span class="invisible">RYj4</span></a>), and</p><p>- A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games by Sokota and D&#39;Orazio et al. (<a href="https://openreview.net/forum?id=ndZ42T8iUmd" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=ndZ42T</span><span class="invisible">8iUmd</span></a>),</p>