Deep reinforcement learning (DRL) has been the driving force behind several well-known machine learning achievements. Frequently stumpy data productivity and the narrow scope of the procedures. It generates to prevent it from being used more widely. The improvement of more effective reinforcement learning algorithms by means of a machine learning (ML) task in and of itself as Meta-RL. Research on Meta-RL is most commonly conducted in problem settings where a task distribution is given. The objective is to acquire a policy with minimal data that can acclimatize to any innovative task from the task distribution. The accelerator shows extremely low latency performance together with training as well as inference operations, empowered by training tasks and inference tasks with parallelism, on-chip weights, pipelined training, and restate memory, and multilevel imitation via parallelism. The DRL FPGA embeds the experience with the replay technique. This model-free approach has the advantage of neither requiring a thorough understanding of the environment for instant issue solutions. The comparison of A2C and A3C with the Meta-RL concept is also included in this paper. Finally, the unresolved issues that need to be resolved before the Meta-RL deep/reinforcement learning practitioner’s regular toolkit are outlined. The anticipated searching times for reinforcement learning through A2C and A3C using Meta-RL assessments, convergence time comparison, and reward function have been done. This can be implemented on the Xilinx Zynq ZCU104 kit using HLS. Better latency with a tenfold lower value in training and inference compared to high-end CPU implementation.