Many actor-critic algorithms build on the standard, on-policy policy gradient formulation to update the actor many of them also consider the entropy of the policy, but instead of maximizing the entropy, they use it as an regularizer incorporating off-policy samples and by using higher order vari...
deep-reinforcement-learningdqnpolicy-gradientreinforcement-learning-algorithmsreinforcementtrpomujocopytorch-rlppotd3pytorch-implementationsoft-actor-critictensorflow2policygradient UpdatedMar 25, 2023 Python araffin/learning-to-drive-in-5-minutes Star286 ...
In the field of reinforcement learning, Soft Actor-Critic (SAC) is an algorithm that has gained significant attention due to its ability to successfully handle both discreteand continuous action spaces. SAC utilizes the actor-critic architecture to simultaneously learn a policy and a value function....
The Actor-Critic model was implemented on a PC with the following specifications: CPU: Intel(R) Core(TM) i7-4790, Memory: 8GB, GPU: GTX1070. Both the actor and critic models consisted of three-layer feed-forward neural network structures with two hidden layers, each containing 256 nodes. ...
Because soft actor-critic learns robust policies, due to entropy maximization at training time, the policy can readily generalize to these perturbations without any additional learning. 动图看原文 The Minitaur robot (Google Brain, Tuomas Haarnoja, Sehoon Ha, Jie Tan, and Sergey Levine)....
Soft Actor-Critic, the new Reinforcement Learning Algorithm from the folks at UC Berkley has been making a lot of noise recently. The algorithm not only boasts of being more sample efficient than traditional RL algorithms but also promises to be robust to brittleness in convergence. In this blog...
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimi... Enders, Tobias,Harrison, James,Schiffer, Maximilian 被...
A segmented parking training framework (SPTF) based on soft actor-critic (SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more ...
The approach is based on the soft-actor critic (SAC) algorithm, which learns a policy for the dynamic adaptation of CPs during the search process. Furthermore, velocity clamping prevents particle velocities from growing unboundedly. In conclusion, the velocity-clamped soft-actor critic self-...
In conclusion, combined with a soft actor–critic and reconfigurable metasurface, we proposed and designed an SAC-M-driven adaptive focusing system. The agent learns and improves policies in real time in changing environments, and the metasurface is guided by it and exhibits effective and robust ad...