1. A computer-implemented system for implementing predetermined reinforcement learning operations, said system comprising: a first processor configured to create at least one reinforcement learning agent and at least one corresponding reinforcement learning environment, said first processor further configured to assign a reinforcement learning agent ID to said reinforcement learning agent, and a reinforcement learning environment ID to said reinforcement learning environment; a first memory module communicably coupled to said first processor, said first memory module configured to store an application-domain specific instruction set (ASI), said application-domain specific instruction set comprising instructions for implementing said predetermined reinforcement learning operations, each of said instructions incorporating at least one of said reinforcement learning agent ID and said reinforcement learning environment ID as an operand; a complex instruction fetch and decode (CISFD) unit communicably coupled to said memory module, said CISFD unit configured to decode at least one of said instructions, and generate a plurality of predetermined threads corresponding to decoded instruction, said CISFD unit still further configured to embed into the predetermined threads, at least one of said reinforcement learning agent ID and reinforcement learning environment ID associated with the decoded instruction; a second processor configured to execute said predetermined reinforcement learning operations, said second processor comprising a plurality of cores, said second processor configured to process each of said predetermined threads in parallel using said plurality of cores, and apply a corresponding instruction onto at least one of a reinforcement learning agent and reinforcement learning environment identified by the reinforcement learning agent ID and reinforcement learning environment ID embedded in said predetermined threads, said second processor further configured to determine at least one action, at least one state-value function, at least one Q-value and at least one reward value corresponding to the reinforcement learning agent, based on execution of the instruction corresponding to said predetermined threads; a second memory module cooperating with said second processor and partitioned into a plurality of memory partitions, each of said memory partitions respectively storing said actions, state-value function, Q-value and reward value; and wherein said second processor is communicably coupled to a neural network via a corresponding neural network data path, said second processor further configured to train said neural network to approximate at least one of a reward function and a state-value function, based on said action, state-value function, Q-value and reward value.