We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Control With Deep Reinforcement Learning to verify its results. We also strive to explain the necessary machine learning framework needed to understand the algorithm. It is a model-free, actor-critic algorithm that implements target networks and mini batch learning from a replay buffer to increase stability. Batch normalisation is introduced to make the algorithm versatile and applicable to multiple environments with varying value ranges and physical units. We use neural networks as function approximators to handle the large state and action spaces. We can show that the algorithm can learn and solve multiple environments using the same set up. Af...
Deep deterministic policy gradient algorithm operating over continuous space of actions has attracte...
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems...
Off-policy model-free deep reinforcement learning methods using previously collected data can improv...
We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Cont...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with c...
Recently, Deep Deterministic Policy Gradient (DDPG) is a popular deep reinforcement learning algorit...
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) ha...
In control, the objective is to find a mapping from states to actions that steer a system to a desir...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
Recent advancements in deep reinforcement learning for real control tasks have received interest fro...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
We extend Deep Deterministic Policy Gradient, a state of the art algorithm for continuous control, i...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
Recent advances of actor-critic methods in deep reinforcement learning have enabled performing sever...
Deep deterministic policy gradient algorithm operating over continuous space of actions has attracte...
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems...
Off-policy model-free deep reinforcement learning methods using previously collected data can improv...
We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Cont...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with c...
Recently, Deep Deterministic Policy Gradient (DDPG) is a popular deep reinforcement learning algorit...
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) ha...
In control, the objective is to find a mapping from states to actions that steer a system to a desir...
International audienceThis paper establishes the link between an adaptation of the policy iteration ...
Recent advancements in deep reinforcement learning for real control tasks have received interest fro...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
We extend Deep Deterministic Policy Gradient, a state of the art algorithm for continuous control, i...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
Recent advances of actor-critic methods in deep reinforcement learning have enabled performing sever...
Deep deterministic policy gradient algorithm operating over continuous space of actions has attracte...
Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems...
Off-policy model-free deep reinforcement learning methods using previously collected data can improv...