Daan Wierstra, David Silver, Yuval Tassa, Tom Erez, Nicolas Heess, Alexander Pritzel, Jonathan J. Hunt, Timothy P. Lillicrap - 2015

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We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.

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Implemented a deep deterministic policy gradient with a neural network for the OpenAI gym pendulum environment.

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Repository for Planar Bipedal walking robot in Gazebo environment using Deep Deterministic Policy Gradient(DDPG) using TensorFlow.

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This is a TensorFlow implementation of DeepMind's A Distributional Perspective on Reinforcement Learning.(C51-DDPG)

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Deep Reinforcement Learning Agent that solves a continuous control task using Deep Deterministic Policy Gradients (DDPG)

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Continuous control with deep reinforcement learning - Deep Deterministic Policy Gradient (DDPG) algorithm implemented in OpenAI Gym environments

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Two Deep Reinforcement Learning agents that collaborate so as to learn to play a game of tennis.

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Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

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practice about reinforcement learning, including Q-learning, policy gradient, deterministic policy gradient and deep deterministic policy gradient

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Repository for Planar Bipedal walking robot in Gazebo environment using Deep Deterministic Policy Gradient(DDPG) using TensorFlow.

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Two agents cooperating to avoid loosing the ball, using Deep Deterministic Policy Gradient in Unity environment

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Examples of published reinforcement learning algorithms in recent literature implemented in TensorFlow

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Simple scripts concern about continuous action DQN agent for vrep simluating domain

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Implementation of Deep Deterministic Policy Gradients using TensorFlow and OpenAI Gym

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DDPG implementation for collaboration and competition for a Tennis environment.

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Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

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Distributed Tensorflow Implementation of Continuous control with deep reinforcement learning (DDPG)

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Implementation of Deep Deterministic Policy Gradient algorithm in Unity environment

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Implementation of DDPG (Modified from the work of Patrick Emami) - Tensorflow (no TFLearn dependency), Ornstein Uhlenbeck noise function, reward discounting, works on discrete & continuous action spaces