def run_controller(self, horizon, policy):
logs = DotMap()
logs.states = []
logs.actions = []
logs.rewards = []
logs.times = []
logs.obs = []
observation = self._env.reset()
print("Env has been reset")
for t in range(horizon):
# env.render()
state = self.state2touch(observation)
print("Go from state to touch")
# print(state)
action = policy.act(state)
print("Get an action based on policy")
observation, reward, done, info = self._env.step(action)
print("Perform an action")
# Log
# logs.times.append()
logs.actions.append(action.tolist())
logs.rewards.append(reward)
logs.states.append(state)
logs.obs.append(observation)
# Cluster state
logs.actions = np.array(logs.actions)
logs.rewards = np.array(logs.rewards)
logs.states = np.array(logs.states)
logs.obs = np.array(logs.obs)
return logs
def run_controller(env, horizon, policy, video = False):
"""
Runs a Reacher3d gym environment for horizon timesteps, making actions according to policy
:param env: A gym object
:param horizon: The number of states forward to look
:param policy: A policy object (see other python file)
"""
# WHat is going on here?
# nol 29 feb - action only acts on first 5 variables
def obs2q(obs):
return obs[0:5]
logs = DotMap()
logs.states = []
logs.actions = []
logs.rewards = []
logs.times = []
observation = env.reset()
for t in range(horizon):
if(video):
env.render()
state = observation
action, t = policy.act(obs2q(state))
# print(action)
observation, reward, done, info = env.step(action)
# Log
# logs.times.append()
logs.actions.append(action)
logs.rewards.append(reward)
logs.states.append(observation)
# Cluster state
logs.actions = np.array(logs.actions)
logs.rewards = np.array(logs.rewards)
logs.states = np.array(logs.states)
return logs
plt.title('begin')
plt.imshow(goal_log['states'][0])
plt.subplot(1, 2, 2)
plt.title('end')
plt.imshow(goal_log['states'][-1])
#goal_img = goal_log['states'][-1]
#goal_diff_img
goal_img = goal_log['states'][-1] - goal_log['states'][-2]
obs_init = sim._env.reset()
state_init = sim.state2touch(obs_init)
#Init error to be massive
error = 1e+3
logs = DotMap()
logs.states = []
logs.actions = []
logs.rewards = []
logs.times = []
logs.obs = []
ii = 0
while (error > parsed.thresh):
#for ii in range(200):
#we take random action for 0th step
if ii == 0:
action = np.random.uniform(-10, 10, 2)
error = 1e+3
elif ii == 1:
curr_img = logs['states'][-1] - state_init
error, action = compute_action(goal_img, curr_img)
else:
curr_img = logs['states'][-1] - logs['states'][-2]
