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Title: GitHub - binaryknight/DeepRL: Modularized Implementation of Deep RL Algorithms in PyTorch

Open Graph Title: GitHub - binaryknight/DeepRL: Modularized Implementation of Deep RL Algorithms in PyTorch

X Title: GitHub - binaryknight/DeepRL: Modularized Implementation of Deep RL Algorithms in PyTorch

Description: Modularized Implementation of Deep RL Algorithms in PyTorch - binaryknight/DeepRL

Open Graph Description: Modularized Implementation of Deep RL Algorithms in PyTorch - binaryknight/DeepRL

X Description: Modularized Implementation of Deep RL Algorithms in PyTorch - binaryknight/DeepRL

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https://patch-diff.githubusercontent.com/binaryknight/DeepRL#references
Human Level Control through Deep Reinforcement Learninghttps://www.nature.com/nature/journal/v518/n7540/full/nature14236.html
Asynchronous Methods for Deep Reinforcement Learninghttps://arxiv.org/abs/1602.01783
Deep Reinforcement Learning with Double Q-learninghttps://arxiv.org/abs/1509.06461
Dueling Network Architectures for Deep Reinforcement Learninghttps://arxiv.org/abs/1511.06581
Playing Atari with Deep Reinforcement Learninghttps://arxiv.org/abs/1312.5602
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descenthttps://arxiv.org/abs/1106.5730
Deterministic Policy Gradient Algorithmshttp://proceedings.mlr.press/v32/silver14.pdf
Continuous control with deep reinforcement learninghttps://arxiv.org/abs/1509.02971
High-Dimensional Continuous Control Using Generalized Advantage Estimationhttps://arxiv.org/abs/1506.02438
Hybrid Reward Architecture for Reinforcement Learninghttps://arxiv.org/abs/1706.04208
Trust Region Policy Optimizationhttps://arxiv.org/abs/1502.05477
Proximal Policy Optimization Algorithmshttps://arxiv.org/abs/1707.06347
Emergence of Locomotion Behaviours in Rich Environmentshttps://arxiv.org/abs/1707.02286
Action-Conditional Video Prediction using Deep Networks in Atari Gameshttps://arxiv.org/abs/1507.08750
A Distributional Perspective on Reinforcement Learninghttps://arxiv.org/abs/1707.06887
Distributional Reinforcement Learning with Quantile Regressionhttps://arxiv.org/abs/1710.10044
The Option-Critic Architecturehttps://arxiv.org/abs/1609.05140
Addressing Function Approximation Error in Actor-Critic Methodshttps://arxiv.org/abs/1802.09477
DeepMind Control Suitehttps://arxiv.org/abs/1801.00690
OpenAI Baselineshttps://github.com/openai/baselines
Ilya Kostrikovhttps://github.com/ikostrikov/pytorch-a2c-ppo-acktr
https://patch-diff.githubusercontent.com/binaryknight/DeepRL#code-of-my-papers
Generalized Off-Policy Actor-Critichttps://arxiv.org/abs/1903.11329
Geoff-PAChttps://github.com/ShangtongZhang/DeepRL/tree/Geoff-PAC
TD3-randomhttps://github.com/ShangtongZhang/DeepRL/tree/TD3-random
DAC: The Double Actor-Critic Architecture for Learning Optionshttps://arxiv.org/abs/1904.12691
DAChttps://github.com/ShangtongZhang/DeepRL/tree/DAC
QUOTA: The Quantile Option Architecture for Reinforcement Learninghttps://arxiv.org/abs/1811.02073
QUOTA-discretehttps://github.com/ShangtongZhang/DeepRL/tree/QUOTA-discrete
QUOTA-continuoushttps://github.com/ShangtongZhang/DeepRL/tree/QUOTA-continuous
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Searchhttps://arxiv.org/abs/1811.02696
ACEhttps://github.com/ShangtongZhang/DeepRL/tree/ACE
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