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# Predict action probabilities and estimated future rewards |
# from environment state |
action_probs, critic_value = model(state) |
critic_value_history.append(critic_value[0, 0]) |
# Sample action from action probability distribution |
action = np.random.choice(num_actions, p=np.squeeze(action_probs)) |
action_probs_history.append(tf.math.log(action_probs[0, action])) |
# Apply the sampled action in our environment |
state, reward, done, _ = env.step(action) |
rewards_history.append(reward) |
episode_reward += reward |
if done: |
break |
# Update running reward to check condition for solving |
running_reward = 0.05 * episode_reward + (1 - 0.05) * running_reward |
# Calculate expected value from rewards |
# - At each timestep what was the total reward received after that timestep |
# - Rewards in the past are discounted by multiplying them with gamma |
# - These are the labels for our critic |
returns = [] |
discounted_sum = 0 |
for r in rewards_history[::-1]: |
discounted_sum = r + gamma * discounted_sum |
returns.insert(0, discounted_sum) |
# Normalize |
returns = np.array(returns) |
returns = (returns - np.mean(returns)) / (np.std(returns) + eps) |
returns = returns.tolist() |
# Calculating loss values to update our network |
history = zip(action_probs_history, critic_value_history, returns) |
actor_losses = [] |
critic_losses = [] |
for log_prob, value, ret in history: |
# At this point in history, the critic estimated that we would get a |
# total reward = `value` in the future. We took an action with log probability |
# of `log_prob` and ended up recieving a total reward = `ret`. |
# The actor must be updated so that it predicts an action that leads to |
# high rewards (compared to critic's estimate) with high probability. |
diff = ret - value |
actor_losses.append(-log_prob * diff) # actor loss |
# The critic must be updated so that it predicts a better estimate of |
# the future rewards. |
critic_losses.append( |
huber_loss(tf.expand_dims(value, 0), tf.expand_dims(ret, 0)) |
) |
# Backpropagation |
loss_value = sum(actor_losses) + sum(critic_losses) |
grads = tape.gradient(loss_value, model.trainable_variables) |
optimizer.apply_gradients(zip(grads, model.trainable_variables)) |
# Clear the loss and reward history |
action_probs_history.clear() |
critic_value_history.clear() |
rewards_history.clear() |
# Log details |
episode_count += 1 |
if episode_count % 10 == 0: |
template = \"running reward: {:.2f} at episode {}\" |
print(template.format(running_reward, episode_count)) |
if running_reward > 195: # Condition to consider the task solved |
print(\"Solved at episode {}!\".format(episode_count)) |
break |
running reward: 8.82 at episode 10 |
running reward: 23.04 at episode 20 |
running reward: 28.41 at episode 30 |
running reward: 53.59 at episode 40 |
running reward: 53.71 at episode 50 |
running reward: 77.35 at episode 60 |
running reward: 74.76 at episode 70 |
running reward: 57.89 at episode 80 |
running reward: 46.59 at episode 90 |
running reward: 43.48 at episode 100 |
running reward: 63.77 at episode 110 |
running reward: 111.13 at episode 120 |
running reward: 142.77 at episode 130 |
running reward: 127.96 at episode 140 |
running reward: 113.92 at episode 150 |
running reward: 128.57 at episode 160 |
running reward: 139.95 at episode 170 |
running reward: 154.95 at episode 180 |
running reward: 171.45 at episode 190 |
running reward: 171.33 at episode 200 |
running reward: 177.74 at episode 210 |
running reward: 184.76 at episode 220 |
running reward: 190.88 at episode 230 |
running reward: 154.78 at episode 240 |
running reward: 114.38 at episode 250 |
running reward: 107.51 at episode 260 |
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