An adaptive radar system comprising: a transmitter unit adapted to transmit a radar signal; a receiver unit adapted to receive a reflected radar signal; a reinforcement learning engine adapted to compute a control policy used to control signal parameters and waveforms of the radar signal transmitted by the transmitter unit; a radar state estimation engine which is adapted to estimate a radar scene depending on the reflected radar signal received by the receiver unit, to provide a target response of a target and a clutter response of a clutter from the radar signal received by the receiver unit of said adaptive radar system, to categorize the target response to extract target state criteria, and to categorize the clutter response to extract clutter state criteria, wherein said radar state estimation engine is configured to apply a radar scene state represented by the extracted target state criteria and the extracted clutter state criteria to a memory storing a radar scene state to waveform lookup table; an adaptive scheduler; a data memory comprising a library of adaptive waveforms with an associate set of signal parameters; wherein if a matching entry for the applied radar scene state is found in the radar scene state to waveform lookup table of the memory the adaptive scheduler is configured to read an associated waveform with a corresponding set of signal parameters from the data memory and to transmit said read waveform and signal parameters as an exploit waveform within a sequence of waveforms forming the radar signal to be transmitted by the transmitter unit; and wherein if a matching entry is found the scheduler is further configured to transmit explore waveforms according to the control policy computed by the reinforcement learning engine to the transmitter unit so that the radar signal transmitted by the transmitter unit comprises a sequence of interleaved exploit waveforms and explore waveforms. a transmitter unit adapted to transmit a radar signal; a receiver unit adapted to receive a reflected radar signal; a reinforcement learning engine adapted to compute a control policy used to control signal parameters and waveforms of the radar signal transmitted by the transmitter unit; a radar state estimation engine which is adapted to estimate a radar scene depending on the reflected radar signal received by the receiver unit, to provide a target response of a target and a clutter response of a clutter from the radar signal received by the receiver unit of said adaptive radar system, to categorize the target response to extract target state criteria, and to categorize the clutter response to extract clutter state criteria, wherein said radar state estimation engine is configured to apply a radar scene state represented by the extracted target state criteria and the extracted clutter state criteria to a memory storing a radar scene state to waveform lookup table; an adaptive scheduler; a data memory comprising a library of adaptive waveforms with an associate set of signal parameters; wherein if a matching entry for the applied radar scene state is found in the radar scene state to waveform lookup table of the memory the adaptive scheduler is configured to read an associated waveform with a corresponding set of signal parameters from the data memory and to transmit said read waveform and signal parameters as an exploit waveform within a sequence of waveforms forming the radar signal to be transmitted by the transmitter unit; and wherein if a matching entry is found the scheduler is further configured to transmit explore waveforms according to the control policy computed by the reinforcement learning engine to the transmitter unit so that the radar signal transmitted by the transmitter unit comprises a sequence of interleaved exploit waveforms and explore waveforms.