for i in range(NUM_EPISODES):
state = env.reset()
#state = convert(env.agentPos)
steps = 0
while True:
if np.random.sample() < EPSILON:
action = np.random.choice(env.action_space.n)
else:
action = np.argmax(Q[state])
next_state, reward, done, _ = env.step(action)
#next_state = convert(env.agentPos)
#if done and reward > 0:
# next_state = 15
#env.render()
print(M)
stats.episode_rewards[i] += reward
stats.episode_lengths[i] = steps
Q[state][action] = np.dot(M[next_state, :], w)
# compute TD error in V
V_target = reward + np.dot(M[next_state, :], w)
V_error = V_target - np.dot(M[state, :], w)
regularize=False,
randomization_space=[rp],
goal_reward=GOAL_REWARD,
lava_reward=LAVA_REWARD,
step_reward=STEP_REWARD,
out_of_grid=OUT_OF_GRID_REWARD,
max_episode_steps=10)
obs, _ = env.reset()
score = 0
for i in range(10000):
action, _ = agent.act(torch.FloatTensor(obs).to(device))
if i == 0:
first_action = action
obs, rew, done, info = env.step(action)
score += rew
if done:
obs, _ = env.reset()
break
first_actions.append(action)
scores.append(score)
print(scores, first_actions)
if all(np.array(scores) >= 0):
same_actions.append(
int(all(elem == first_actions[0] for elem in first_actions)))
np.save('results/' + str(NAME) + '_vpg.npy', np.array(same_actions))
plt.hist(same_actions)
agent.reset()
agent.append_trajectory(t_step=0,
prev_action=None,
observation=obs,
reward=None,
done=None)
prev_action = agent.pick_action(obs)
while True:
# --- time step rolls here ---
#print('---- time step {0} ----'.format(env.t_step))
obs, reward, done = env.step(prev_action)
#print(agent.V)
agent.append_trajectory(t_step=env.t_step,
prev_action=prev_action,
observation=obs,
reward=reward,
done=done)
agent.eval_td() # learn from history
#print(agent.V)
if done:
break
