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Deep reinforcement learning (DRL)-based methods have been widely used to learn optimal bidding and/or pricing strategies of load serving entities (LSEs) in electricity markets. However, previous studies on joint bidding and pricing (JBP) problem have been limited to model-based methods for price-maker LSEs or model-free methods for price-taker LSEs. In the context of addressing this research gap, this paper explores for the very first time a model-free multi-agent reinforcement learning (MARL)-based approach for the price-maker JBP problem. The original problem is first formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where multiple agents are trained to find the optimal joint strategy in a fully cooperative setting. In order to overcome the challenges such as credit assignment and coordination, this paper proposes a parallelizable deep MT-MARL framework by …