在评估过程中,SQL与多种先进的离线强化学习(RL)方法进行了比较,包括BC(行为克隆),10%BC,BCQ(Batch Constrained Q-learning),DT(Decision Transformer),TD3+BC,One-step RL,CQL(Conservative Q-learning)和IQL(Implicit Q-Learning)。这些比较的结果显示,SQL在复杂的任务(如AntMaze和Kitchen)中表现优越,而在性能...
这一思路源自自然语言处理中的Transformer模型,通过将过去的序列信息输入模型,预测未来的动作。该方法的代表作是决策Transformer(Decision Transformer, DT),它将离线强化学习转化为一个有监督学习问题,通过历史状态、动作和回报的序列来预测最优的未来动作。虽然CSM方法在某些任务中表现优异,但它在面对次优数据拼接(stitchi...
这个初级智能体在 95% 的游戏中击败了内置的「精英」AI 关卡(相当于人类玩家的黄金级别)。 AlphaStar 神经网络结构将 Transformer 框架运用于模型单元(类似于关系深度强化学习),结合一个深度 LSTM 核心、一个带有 pointer network 的自回归策略前端和一个集中的值基线。超强的网络设计使得其适合长期序列建模和大输出空...
Mathematician Richard Bellman invented this equation in 1957 as a recursive formula for optimal decision-making. In the q-learning context, Bellman's equation is used to help calculate the value of a given state and assess its relative position. The state with the highest value is considered the...
Compared with Markov Decision Processes (MDP), agents in POMDP cannot fully receive information from the environment, which is an obstacle to traditional RL algorithms. One solution is to establishes a sequence-to-sequence model. As the core of deep Q-networks, Transformer has achieved certain ...
Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate. 展开 关键词: MARKOV processes ERROR rates DECISION making RADAR RADAR ...
Decay factors and a greedy strategy are utilized to perturb the decision-making of the intelligent agent, preventing it from falling into local optima while simultaneously facilitating extensive exploration of the solution space. Finally, the proposed method proved to be effective in solving the open-...
Transformer research developments in RL for decision-making.We hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future directions.To keep track of the rapid TransRL developments in the decision-making domains,we summarize the ...
Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning, employs the clipped double estimator to approximate the m... H Jiang,J Xie,J Yang 被引量: 0发表: 2022年 加载更...
This paper proposes a novel framework that integrates the strengths of BERT (Bidirectional Encoder Representations from Transformers) GPT (Generative Pre-trained Transformer), and Graph Recurrent Q Learning Network (GRQLN) for developing a dynamic and efficient ontology tool. BERT-GPT, with its ...