An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of the coming offer based on previous offer exchanges and remaining time.
Apr 26, 2022 · Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement ...
Our model learns to produce the target utility of the coming offer based on previous offer exchanges and remaining time. Furthermore, an imitation learning ...
This work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation ...
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Jan 7, 2022 · We use deep reinforcement learning throughout an actor-critic architecture to estimate the tactic parameter values for a threshold utility ...
This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity ...
In bilateral negotiations us- ing the AOP, the two agents take turns proposing bids. When one agent proposes to the other agent, the pro- posed agent selects ...
We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic ...
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Adaptive Strategy Templates Using Deep Reinforcement Learning for ...
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Aug 4, 2023 · We introduce a novel negotiation model enabling an agent to learn a heuristic strategy from a given set of predefined tactics, ...