Delete CIO_Decision.m

CIO_Decision.m

-function action = DDPG_CIO_Decision(k,cn,c_req)
-num_episodes = 100;
-gamma = 0.99;
-actor_lr = 0.001;
-critic_lr = 0.01;
-CIO_min = -6;
-CIO_max = 6;
-actornet = create_actor_network(k);
-criticnet = create_critic_network(k);
-obsInfo = rlNumericSpec([k+1 1]);
-actInfo = rlNumericSpec([k 1],'LowerLimit', 0, 'UpperLimit', 1); 
-actor = rlContinuousDeterministicActor(actornet,obsInfo,actInfo);
-critic = rlQValueFunction(criticnet,obsInfo,actInfo,...
-    ObservationInputNames="netOin", ...
-    ActionInputNames="netAin");
-agentOptions = rlDDPGAgentOptions(...
-    'TargetSmoothFactor', 1e-3, ...
-    'SampleTime', 0.01, ...
-    'ExperienceBufferLength', 1e6, ...
-    'DiscountFactor', 0.99, ...
-    'MiniBatchSize', 64, ...
-    'NumStepsToLookAhead', 1);
-agent = rlDDPGAgent(actor, critic, agentOptions);
-trainOpts = rlTrainingOptions(...
-    MaxEpisodes=100,...
-    MaxStepsPerEpisode=100,...
-    StopTrainingCriteria="AverageReward",...
-    StopTrainingValue=2000);
-replay_buffer = [];
-state = initialize_state(k+1,CIO_min,CIO_max);
-for episode = 1:num_episodes
-        action = select_action(actornet, state, CIO_min, CIO_max);
-        [next_state, reward] = perform_action_prop(state, action,cn,c_req,CIO_min,CIO_max);
-        experience = struct('state', state, 'action', action, 'reward', reward, 'next_state', next_state);
-        replay_buffer = [replay_buffer, experience];
-        criticnet = update_critic(actornet, criticnet, replay_buffer, critic_lr, gamma,CIO_min,CIO_max);
-        actornet = update_actor(actornet, criticnet, replay_buffer, actor_lr, gamma, CIO_min,CIO_max);
-        state = next_state;
-end
-end
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