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추적: • 2017-03_model-agnostic_meta-learning_for_fast_adaptation_of_deep_networks • 2016-11_quasi-recurrent_neural_networks • apt • illuminating_mario_scenes_in_the_latent_space_of_a_generative_adversarial_network • install • syntax • recovery • 2024-07_autoverse_an_evolvable_game_langugage_for_learning_robust_embodied_agents • start • 2024-01_mixtral_of_experts

mcts

TAG: mcts

  • 2020-10 [MuZero] Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
2020/07/12 04:42Hyunsoo Park
  • 2021-07 Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
2021/07/06 17:24Hyunsoo Park
  • 2021-07 Vector Quantized Models for Planning
2021/08/05 06:03Hyunsoo Park
  • 2023-08 MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games
2024/04/30 00:18Hyunsoo Park
  • 2024-01 Monte Carlo Tree Search for Recipe Generation using GPT-2
2024/01/11 20:14Hyunsoo Park
  • 2024-04 Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
2024/04/23 00:20Hyunsoo Park
  • 2024-04 Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
2024/04/24 00:22Hyunsoo Park
  • Collaborative Agent Gameplay in the Pandemic Board Game
2021/03/23 16:03Hyunsoo Park
  • Python: LaMCTS
2021/07/20 04:12Hyunsoo Park
mcts.txt · 마지막으로 수정됨: 2024/03/23 02:38 저자 127.0.0.1

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