最后,在实际应用中,作者维护了两个单独训练的soft Q-networks,并以其两个输出的最小值作为soft Q-network的输出。他们这样做是因为Fujimoto, Hoof, and Meger (2018)表明这有助于遏制state-value的高估。 3 Soft Actor-Critic for Discrete Action Settings (SAC-Discrete) 现在,我们得出上述SAC算法的离散动作版本。
我们进行如下操作:首先,我们解释Haarnoja et al. (2018)以及Haarnoja et al. (2019)发现的连续动作设置中的SAC,然后我们导出并解释创建算法的离散动作版本所需的更改,最后我们在Atari套件上测试离散动作算法。 2 Soft Actor-Critic SAC [Haarnoja et al., 2018]试图找到一种最大化最大熵目标的策略: 为了最大...
Soft Actor-Critic is a state-of-the-art reinforcement learning algorithm for continuous action settings that is not applicable to discrete action settings. Many important settings involve discrete actions, however, and so here we derive an alternative version of the Soft Actor-Critic algorithm that ...
In this paper, we change it by proposing a practical discrete variant of the soft actor-critic (SAC) algorithm. The new variant enables off-policy learning using policy heads for discrete domains. By incorporating it into the advanced Rainbow variant, i.e., the "bigger, better, faster" (...
Actor–Critic-Based Optimal Tracking for Partially Unknown Nonlinear Discrete-Time Systems 来自 国家科技图书文献中心 喜欢 0 阅读量: 456 作者:B Kiumarsi,Lewis, F.L.摘要: This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) ...
图8 Actor Critic 也就是对于给定的 一个h,a,z(都可以使用GRU一步一步往后预测 imagination horizon H = 15的情况,然后就根据预测每一步的\hat{r}, \hat{\gamma},\hat{z}_{t}来预测的V值,感觉这里就是体现dream的地方,可以根据world model想象H步后的情况,并且拿想象的得到的状态的V值来更新critic和...
# discrete_sac_agent.py """ A Soft Actor-Critic Agent. Implements the discrete version of Soft Actor-Critic (SAC) algorithm based on "Discrete and Continuous Action Representation for Practical RL in Video Games" by Olivier Delalleau, Maxim Peter, Eloi Alonso, Adrien Logut (2020). Paper:...
Actor-critic is a reinforcement learning method that can solve such problems through online iteration. This paper proposes an online iterative algorithm for solving linear discrete-time systems graphics games with input constraints, and this algorithm without the need for drift dynamics of agents. Each...
An actor-critic neural network framework for implementing the developed model-free optimal consensus control method is constructed to approximate the local Q-functions and the control policies. Finally, the feasibility and effectiveness of the developed method are verified by a series of simulations. ...
agent = rlACAgent(actor,critic); Check the agent with a random observation input. getAction(agent,{rand(obsInfo.Dimension)}) ans =1x1 cell array{[-10]} Specify agent options, including training options for the actor and critic, using dot notation. Alternatively, you can userlACAgentOptionsan...