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for _ in range(5000): | |
observations += [observation.tolist()] # Record the observations for normalization and replay | |
if done: # If the simulation was over last iteration, exit loop | |
break | |
# Pick an action according to the policy matrix | |
outcome = np.dot(policy, observation) | |
action = 1 if outcome > 0 else 0 | |
# Make the action, record reward | |
observation, reward, done, info = env.step(action) | |
score += reward | |
return score, observations |
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