Deep Reinforcement Learning Algorithms Here you can find several projects dedicated to the Deep Reinforcement Learning methods. The projects are deployed in the matrix form: [env x model], where env is the environment to be solved, and model is the model/algorithm which solves this environment. ...
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4.1.x2.medicine related machine learning 4.2.deep learning(DL) 4.2.1.fundamentals of deep learning 4.2.2.neural networks(NN) 4.3.6.other algorithms 4.3.7.deep Q network::DQN 4.3.8.Policy Gradient 4.3.9.Actor Critic 4.4.supervised and unsupervised learning 4.0.machine learning&deep learning&re...
GitHub - songrotek/async-rl: An attempt to reproduce the results of "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783) GitHub - songrotek/rllab: rllab is a framework for developing and evaluating reinforcement learning algorithms. GitHub - songrotek/DRL-FlappyBi...
rl algorithms: arguments.py: contain the parameters used in the training. <rl-name>_agent.py: contain the most important part of the reinforcement learning algorithms. models.py: the network structure for the policy and value function.
GitHub - songrotek/rllab: rllab is a framework for developing and evaluating reinforcement learning algorithms. GitHub - songrotek/DRL-FlappyBird: Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow) ...
Learning goal directed behaviour in environments with sparse feedback is a major challenge for reinforcement learning algorithms, and one of the key difficulties is insufficient exploration that results in an agent being unable to learn robust policies: intrinsically motivated agents can explore new ...
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In this work, we exploit Deep Q-Learning (DQL)45 and Proximal Policy Optimization (PPO)46 algorithms to train the agents, depending on the reward function. Such algorithms differ in many aspects, as described in Supplementary Note 1. The former is mandatory for the case of sparse reward, si...
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