Xdiscount = 0
Xepsi = 0
Xdec = 0
rewards = []
for j in learnRates:
for k in discounts:
for ep in epsi:
for epdec in epsi_decay:
params = [episode_count, j, k, ep, epdec]
#params = [5000, 0.001,0.95, 1.1, 0.005]
#params = [5000, 0.0005,0.99, 0.1]
agent = DQNAgent(env.action_space, env.observation_space,
params)
agent._render = False
rewardList, stepList = agent.train(env)
rewards.append(rewardList)
if reward < sum(rewards[-1]) / episode_count:
reward = sum(rewards[-1]) / episode_count
mytrainedAgent = agent
XlearnRate = j
Xdiscount = k
Xepsi = ep
Xdec = epdec
plt.plot(low_pass(rewardList),
label=("Test: " + str(params)))
## Train Agent
#agent._render = False
#rewardList, stepList = agent.train(env)
#agent.saveModel(agent.checkpointpath)
for ep in range(num_episodes):
if (ep % 1000 == 0):
print("episode=", ep)
print("epsilon", sender.epsilon)
obs = env.get_observation()
mex = sender.take_action(obs)
act = receiver.take_action(mex)
rew = env.get_reward(obs, act)
sender.store_transition(obs, mex, rew)
receiver.store_transition(mex, act, rew)
returns.append(rew)
sender.train(ep)
receiver.train(ep)
print("epsilon", sender.epsilon)
print("sender action probabilities")
for s in range(num_act):
print(sender.get_action_probabilities(s))
print("receiver action probabilities")
for s in range(num_act):
print(receiver.get_action_probabilities(s))
for s in range(num_act):
plt.plot(np.linspace(0, num_act, num_act),
sender.get_action_probabilities(s),
label=str(s))