{"id":"arxiv:2106.06860","title":"A Minimalist Approach to Offline Reinforcement Learning","date":"2021-06-12","section":"Reinforcement learning","quality":-0.495051,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/","source_url":"https://arxiv.org/abs/2106.06860","text_available":true,"authors":["Scott Fujimoto","Shixiang Shane Gu"],"outgoing":[{"source":"arxiv:2106.06860","target":"arxiv:1801.01290","evidence":"title","id":"arxiv:1801.01290","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/21e70876ae9603903b2b20dd667a413bdd8b0abf37b9c11e284ea8b5e4124c74/"},{"source":"arxiv:2106.06860","target":"arxiv:1802.09477","evidence":"title","id":"arxiv:1802.09477","title":"Addressing Function Approximation Error in Actor-Critic 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Minimalist Approach to Offline Reinforcement Learning","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-1.html#text"},{"title":"Abstract","offset":59,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-1.html#text"},{"title":"1 Introduction","offset":1131,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-1.html#text"},{"title":"2 Related Work","offset":5628,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-1.html#text"},{"title":"3 Background","offset":9546,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-1.html#text"},{"title":"4 Challenges in Offline RL","offset":12147,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-1.html#text"},{"title":"5 A Minimalist Offline RL Algorithm","offset":19545,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-2.html#text"},{"title":"6 Experiments","offset":23481,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-2.html#text"},{"title":"7 Conclusion","offset":26915,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-2.html#text"},{"title":"Appendix A Broader Impact","offset":28298,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-2.html#text"},{"title":"Appendix B Experimental Details","offset":29364,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-2.html#text"},{"title":"Appendix C Additional Experiments","offset":31963,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/00a35c7a58d8775f23035efbd1cb0a5f540440d47b8a996be8a4fa0da2da3e0e/part-3.html#text"},{"title":"C.1 Additional 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