def main():
env = KukaDiverseObjectEnv(renders=True, isDiscrete=False)
policy = ContinuousDownwardBiasPolicy()
while True:
obs, done = env.reset(), False
print("===================================")
print("obs")
print(obs)
episode_rew = 0
while not done:
env.render(mode='human')
act = policy.sample_action(obs, .1)
print("Action")
print(act)
obs, rew, done, _ = env.step([0, 0, 0, 0, 0])
episode_rew += rew
print("Episode reward", episode_rew)
def main():
env = KukaDiverseObjectEnv(renders=True, isDiscrete=False)
policy = ContinuousDownwardBiasPolicy()
while True:
obs, done = env.reset(), False
print("===================================")
print("obs")
print(obs)
episode_rew = 0
while not done:
env.render()
act = policy.sample_action(obs, .1)
print("Action")
print(act)
obs, rew, done, _ = env.step([0, 0, 0, 0, 0])
episode_rew += rew
print("Episode reward", episode_rew)
ep_reward_list = []
avg_reward_list = []
actor_loss, critic_loss = 0, 0
best_score = -np.inf
print('Main training loop')
for episode in range(start_episode, MAX_EPISODES):
obsv = env.reset()
state = np.asarray(
obsv, dtype=np.float32) / 255.0 # convert into float array
episodic_reward = 0
frames = []
steps = 0
while True:
if episode > MAX_EPISODES - 3:
frames.append(env.render(mode='rgb_array'))
# take an action as per the policy
if episode < RAND_EPS: # explore for some episodes
action = env.action_space.sample()
else:
action = agent.policy(state)
# obtain next state and rewards
next_obsv, reward, done, info = env.step(action)
next_state = np.asarray(
next_obsv,
dtype=np.float32) / 255.0 # convert into float array
#tb_img = np.reshape(next_state, (-1, 48, 48, 3)) # for tensorboard