International audienceIn the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and later extended in Neural Fitted Actor Critic (NFAC). This approach is based on a policy update different from that of deterministic policy gradient (DPG). Previous work has observed its excellent performance empirically, but a theoretical justification is lacking. To fill this gap, we provide a theoretical explanation to motivate this unorthodox policy update by relating it to another update and making explicit the objective function of the latter. We furthermore discuss in depth the properties of these updates to get a deeper understanding o...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is l...
International audienceIn this paper we consider deterministic policy gradient algorithms for reinfor...
International audienceA new off-policy, offline, model-free, actor-critic reinforcement learning alg...
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with c...
International audienceA novel reinforcement learning algorithm that deals with both continuous state...
International audiencePolicy gradient algorithms have proven to be successful in diverse decision ma...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) ha...
We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Cont...
Recently, Deep Deterministic Policy Gradient (DDPG) is a popular deep reinforcement learning algorit...
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on te...
Thesis (Ph.D.)--University of Washington, 2022Sequential decision making, especially in the face of ...
Deep reinforcement learning agents for continuous control are known to exhibit significant instabili...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is l...
International audienceIn this paper we consider deterministic policy gradient algorithms for reinfor...
International audienceA new off-policy, offline, model-free, actor-critic reinforcement learning alg...
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with c...
International audienceA novel reinforcement learning algorithm that deals with both continuous state...
International audiencePolicy gradient algorithms have proven to be successful in diverse decision ma...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) ha...
We reproduce the Deep Deterministic Policy Gradient algorithm presented in the paper Continuous Cont...
Recently, Deep Deterministic Policy Gradient (DDPG) is a popular deep reinforcement learning algorit...
Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on te...
Thesis (Ph.D.)--University of Washington, 2022Sequential decision making, especially in the face of ...
Deep reinforcement learning agents for continuous control are known to exhibit significant instabili...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
Deep Neural Networks (DNNs) can be used as function approximators in Reinforcement Learning (RL). On...
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is l...
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