config['device'] = 'cpu'
# Get the environment and extract the number of actions.
# env = CartPoleEnvCustom()
trainEnv = gym.make("CartPole-v0")
testEnv = gym.make("CartPole-v0")
N_S = trainEnv.observation_space.shape[0]
N_A = trainEnv.action_space.n
netParameter = dict()
netParameter['n_feature'] = N_S
netParameter['n_hidden'] = [40, 40]
netParameter['n_output'] = N_A
actorNet = MultiLayerNetLogSoftmax(netParameter['n_feature'],
netParameter['n_hidden'],
N_A)
criticNet = MultiLayerNetRegression(netParameter['n_feature'],
netParameter['n_hidden'],
1)
optimizer1 = optim.Adam(actorNet.parameters(), lr=config['learningRate'])
optimizer2 = optim.Adam(criticNet.parameters(), lr=config['learningRate'])
agent = ActorCriticTwoNet(actorNet, criticNet, [trainEnv, testEnv], [optimizer1, optimizer2], torch.nn.MSELoss(), N_A, config)
agent.train()
agent.test(100)
N_A = env.nbActions
netParameter = dict()
netParameter['n_feature'] = N_S
netParameter['n_hidden'] = [100]
netParameter['n_output'] = N_A
policyNet = MultiLayerNetRegression(netParameter['n_feature'],
netParameter['n_hidden'],
netParameter['n_output'])
print(policyNet.state_dict())
targetNet = deepcopy(policyNet)
optimizer = optim.Adam(policyNet.parameters(), lr=config['learningRate'])
agent = DQNAgent(policyNet,
targetNet,
env,
optimizer,
torch.nn.MSELoss(),
N_S,
N_A,
config=config)
policy = deepcopy(env.map)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.map[i, j] == 0:
policy[i, j] = -1
config['trainBatchSize'] = 32
config['gamma'] = 0.9
config['learningRate'] = 0.001
config['netGradClip'] = 1
config['logFlag'] = False
config['logFrequency'] = 100
config['priorityMemoryOption'] = False
config['netUpdateOption'] = 'doubleQ'
config['netUpdateFrequency'] = 1
config['priorityMemory_absErrUpper'] = 5
config['numWorkers'] = 4
env = StablizerOneD()
N_S = env.stateDim
N_A = env.nbActions
netParameter = dict()
netParameter['n_feature'] = N_S
netParameter['n_hidden'] = [4]
netParameter['n_output'] = N_A
policyNet = MultiLayerNetRegression(netParameter['n_feature'],
netParameter['n_hidden'],
netParameter['n_output'])
optimizer = SharedAdam(policyNet.parameters(), lr=1.0)
agent = DQNA3CMaster(config, policyNet, env, optimizer,
torch.nn.MSELoss(reduction='none'), N_A)
agent.test_multiProcess()