def learn(self):
agent = DDPGAgent(
env=self.env,
replay_memory_size=REPLAY_MEMORY_SIZE,
learning_rate=LEARNING_RATE,
batch_size=MINIBATCH_SIZE,
gamma=GAMMA,
tau=TAU
)
stats = {'scores': [], 'avg': [], 'min': [], 'max': []}
for ep in tqdm(range(1, self.episodes + 1), ascii=True, unit='episodes'):
print(self.epsilon)
action_stats = [0, 0]
current_state = self.env.reset()
current_state = self.convert_gray(current_state)
done = False
score = 0
steps = 0
while not done:
steps += 1
if np.random.random() > self.epsilon:
action_stats[0] += 1
action = agent.get_action(current_state)
else:
action_stats[1] += 1
action = self.env.action_space.sample()
action[2] = min(action[2], 0.2)
action[1] = action[1]*2
new_state, reward, done, _ = self.env.step(action)
if ep % self.results_every_n_episodes == 0:
self.env.render()
score += reward
new_state = self.convert_gray(new_state)
agent.memory.push(current_state, action, reward, new_state)
if steps % 64 == 0:
agent.update()
current_state = new_state
if self.epsilon > 0.1:
self.epsilon -= self.epsilon_decay_value
if score < 0:
break
print(action_stats)
print(score)
stats['scores'].append(score)
self.env.close()
return agent.actor
## extract data state = env_data["observation"] goal = env_data["desired_goal"] ## logging rewards episode_reward = 0 for step in range(STEPS_PER_EPISODE): ## normalize state and goal # state = normalizer(state, 5.0) # goal = normalizer(goal, 5.0) ## get action from behavioural policy action = agent.get_action(state, goal) if action is not None: action = noise.get_action(action, step) else: action = env.action_space.sample() time.sleep(0.002) next_state, reward, _, _ = env.step(action) env.render() episode_reward += reward ## store transition - Standard Experience Replay state_rep = np.concatenate((state, goal), axis=0) next_state_rep = np.concatenate((next_state["observation"], goal), axis=0)
batch_size = 128
rewards = []
avg_rewards = []
for episode in range(50):
state = env.reset()
#state = state['observation']
#print("state = ", state)
ou_noise.reset()
episode_reward = 0
for step in range(500):
if episode >= 45:
env.render()
#action = agent.get_action(state, ou_noise)
action = agent.get_action(state, ou_noise)
#print("action = ", action)
new_state, reward, done, _ = env.step(action)
#print("new state =", new_state)
#new_state = new_state['observation']
#print("new state =", new_state)
agent.memory.push(state, action, reward, new_state, done)
if len(agent.memory) > batch_size:
agent.train(batch_size)
state = new_state
episode_reward += reward
if done:
if episode == 0:
