两篇论文传送门: 2018年1月挂arXiv,8月被ICML收录,Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 2018年12月挂arXiv,Soft Actor-Critic Algorithms and Applications 2.5 DPG Deterministic Policy Gradient 是确定性策略梯度方法,是off-policy、连续状态、连续动...
It is demonstrated that the predicted average capacity greatly exceeds other baseline heuristic algorithms while strongly converging to the supervised, unparameterized approach. The predicted average channel powers differ only by 0.1 W from the reference ones, while the baselines differ significantly more,...
但是,model-free 算法的样本复杂性,特别是当使用高维的函数估计时,使其应用范围局限在物理系统中。在这种情况下,选择有效的 model-free algorithms 使用更加合适的,特定任务的表示,以及 model-based algorithms 来用监督学习的方法来学习系统的模型,并且在该模型下进行策略的优化。利用特定任务的表示显著的改善了效率,...
Arguably, this is not the most efficient way to find an optimal policy and in fact, several methods exist for combining model-free reinforcement learning with inverse reinforcement learning (IRL) algorithms, which are used to infer a reward function given state-action pairs sampled from an optimal...
One of the drawbacks of DQN, and of model-free RL algorithms in general, is that it requires a non-negligible amount of hyperparameter tuning and offline training before it can be deployed on the controlled environment. For our experiments, we tuned and trained DQN for each one of the rout...
General flow of DRL-based control of CDPRs with model uncertainties Full size image Based on the action state function, the optimal policy can be learned using optimized algorithms (Figure 5) during the training process. Under the optimal policy, the agent observes the current state and selects...
[283] utilized the RL algorithm to the operation optimization of the air-conditioning system and proposed an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithms and a complete application process. Qiu et al. [284] combined RL technology with expert...
Lec10 Model-based PlanningSo in the first part of this course we covered a range of different model free reinforcement learning algorithms. We talked about policy gradient methods, actor-critic algo…
Hence, PPO belongs to the group of actor-critic DRL algorithms. PPO is relatively straightforward to implement and enables accelerated learning from multiple trajectories, i.e., multiple trajectories may be sampled and processed in parallel rather than sequentially to produce new training data T=[τ...
Our effort is toward unifying GAN and DRL algorithms into a unifying AI model (AGI or general-purpose AI or artificial general intelligence which has general-purpose applications to: (A) offline learning (of stored data) like GAN in (un/semi-/fully-)SL setting such as big data analytics (...