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Title: GitHub - StepNeverStop/RLs: Reinforcement Learning Algorithms Based on PyTorch · GitHub

Open Graph Title: GitHub - StepNeverStop/RLs: Reinforcement Learning Algorithms Based on PyTorch

X Title: GitHub - StepNeverStop/RLs: Reinforcement Learning Algorithms Based on PyTorch

Description: Reinforcement Learning Algorithms Based on PyTorch - StepNeverStop/RLs

Open Graph Description: Reinforcement Learning Algorithms Based on PyTorch - StepNeverStop/RLs

X Description: Reinforcement Learning Algorithms Based on PyTorch - StepNeverStop/RLs

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I-DQNhttp://arxiv.org/abs/1511.08779
Value-Decomposition Networks, VDNhttp://arxiv.org/abs/1706.05296
Monotonic Value Function Factorisation Networks, QMIXhttp://arxiv.org/abs/1803.11485
Multi-head Attention based Q-value Mixing Network, Qattenhttp://arxiv.org/abs/2002.03939
Factorize with Transformation, Qtranhttps://arxiv.org/abs/1905.05408
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PPOhttps://arxiv.org/abs/1707.06347
DPPOhttp://arxiv.org/abs/1707.02286,
Trust Region Policy Optimization, TRPOhttps://arxiv.org/abs/1502.05477
Natural Policy Gradient, NPGhttps://proceedings.neurips.cc/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf
Deterministic Policy Gradient, DPGhttps://hal.inria.fr/file/index/docid/938992/filename/dpg-icml2014.pdf
Deep Deterministic Policy Gradient, DDPGhttps://arxiv.org/abs/1509.02971
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Discrete SAChttps://arxiv.org/abs/1910.07207
Tsallis Actor Critic, TAChttps://arxiv.org/abs/1902.00137
Twin Delayed Deep Deterministic Policy Gradient, TD3https://arxiv.org/abs/1802.09477
2013https://arxiv.org/pdf/1312.5602.pdf
2015https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
Double Deep Q-learning Network, DDQNhttps://arxiv.org/abs/1509.06461
Dueling Double Deep Q-learning Network, DDDQNhttps://arxiv.org/abs/1511.06581
Deep Recurrent Q-learning Network, DRQNhttps://arxiv.org/abs/1507.06527
Deep Recurrent Double Q-learning, DRDQNhttps://arxiv.org/abs/1908.06040
Category 51, C51https://arxiv.org/abs/1707.06887
Quantile Regression DQN, QR-DQNhttps://arxiv.org/abs/1710.10044
Implicit Quantile Networks, IQNhttps://arxiv.org/abs/1806.06923
Rainbow DQNhttps://arxiv.org/abs/1710.02298
MaxSQNhttps://github.com/createamind/DRL/blob/master/spinup/algos/maxsqn/maxsqn.py
Soft Q-Learning, SQLhttps://arxiv.org/abs/1702.08165
Bootstrapped DQNhttp://arxiv.org/abs/1602.04621
Averaged DQNhttp://arxiv.org/abs/1611.01929
Option-Critic, OChttp://arxiv.org/abs/1609.05140
Asynchronous Advantage Option-Critic, A2OChttp://arxiv.org/abs/1709.04571
PPO Option-Critic, PPOChttp://arxiv.org/abs/1712.00004
Interest-Option-Critic, IOChttp://arxiv.org/abs/2001.00271
Learning Latent Dynamics for Planning from Pixels, PlaNethttp://arxiv.org/abs/1811.04551
Dream to Control, Dreamerhttp://arxiv.org/abs/1912.01603
Mastering Atari with Discrete World Models, DreamerV2http://arxiv.org/abs/2010.02193
Model-Based Value Estimation, MVEhttp://arxiv.org/abs/1803.00101
Conservative Q-Learning for Offline Reinforcement Learning, CQLhttp://arxiv.org/abs/2006.04779
Discretehttp://arxiv.org/abs/1910.01708
Continuoushttp://arxiv.org/abs/1812.02900
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