  In this paper, we investigate an energy cost minimization problem for
prosumers participating in peer-to-peer energy trading. Due to (i)
uncertainties caused by renewable energy generation and consumption, (ii)
difficulties in developing an accurate and efficient energy trading model, and
(iii) the need to satisfy distribution network constraints, it is challenging
for prosumers to obtain optimal energy trading decisions that minimize their
individual energy costs. To address the challenge, we first formulate the above
problem as a Markov decision process and propose a multi-agent deep
deterministic policy gradient algorithm to learn optimal energy trading
decisions. To satisfy the distribution network constraints, we propose
distribution network tariffs which we incorporate in the algorithm as
incentives to incentivize energy trading decisions that help to satisfy the
constraints and penalize the decisions that violate them. The proposed
algorithm is model-free and allows the agents to learn the optimal energy
trading decisions without having prior information about other agents in the
network. Simulation results based on real-world datasets show the effectiveness
and robustness of the proposed algorithm.
