def main():
# Instanciate specified environment.
env = fe.FlockingEnv(size1, dynamic="first")
# Get environment specs
num_states = (size1 + 1) * dim * 2
num_actions = size1 * dim
# Print specs
print("Number of states: %d" % num_states)
print("Number of actions: %d" % num_actions)
print("-----------------------------------------")
# Instanciate reinforcement learning agent which contains Actor/Critic DNN.
#agents =[]
#for i in range(0,size):
agent = DDPGAgent(ob_shape=num_states, ac_shape=dim)
# agents.append(agent)
# Exploration noise generator which uses Ornstein-Uhlenbeck process.
noise = OUNoise(1)
for i in range(episodes_num):
print("--------Episode %d--------" % i)
reward_per_episode = 0
observation = env.reset_mul()
#observation = env.reset_full()
for j in range(steps_limit):
if is_movie_on: env.render()
# Select action off-policy
state = observation
action = np.zeros((size1, dim), dtype=np.float32)
# get individual ob states here
for k in range(0, size1):
ac = agent.feed_forward_actor(
np.reshape(state[k], [1, num_states]))
# print(noise.generate())
if i % 2 == 0:
action[k][0] = ac[0][0] + noise.generate()
action[k][1] = ac[0][1] + noise.generate()
else:
action[k][0] = ac[0][0] + noise.generate()
action[k][1] = ac[0][1] + noise.generate()
'''
action = agent.feed_forward_actor(np.reshape(state, [1, num_states]))
action = np.reshape(action, [size1,dim])
for k in range(0, size1):
if i % 2 == 0:
action[k][0] += noise.generate()
action[k][1] += noise.generate()
'''
# Throw action to environment
observation, reward, done, info = env.step_mul(action)
#observation, reward, done, info = env.step_full(action)
for k in range(0, size1):
agent.add_experience(np.reshape(state[k],
[num_states]), action[k],
np.reshape(observation[k], [num_states]),
reward[k], done)
#action=np.reshape(action,[num_actions])
#agent.add_experience(np.reshape(state, [num_states]), action,
# np.reshape(observation, [ num_states]), reward, done)
# Train actor/critic network
if len(agent.replay_buffer) > MINI_BATCH_SIZE: agent.train()
reward_per_episode = reward.sum()
if j % 100 == 0:
print(j, "step finished. reward=", reward_per_episode, "info=",
info)
# print("action=",action,"observation=",observation)
if (done or j == steps_limit - 1):
print("Steps count: %d" % j)
print("Total reward: %d" % reward_per_episode)
env.render()
#noise.reset()
with open("reward_log.csv", "a") as f:
f.write("%d,%f\n" % (i, reward_per_episode))
break
def main():
# Instanciate specified environment.
env = gym.make(ENV_NAME)
# Confirm that state and action spaces are continuous
assert isinstance(env.observation_space,
gym.spaces.Box), "State space must be continuous!"
assert isinstance(env.action_space,
gym.spaces.Box), "Action space must be continuous!"
# Get environment specs
num_states = env.observation_space.shape[0]
num_actions = env.action_space.shape[0]
steps_limit = env.spec.timestep_limit
# Print specs
print("-----------Env specs (%s)------------" % ENV_NAME)
print("Number of states: %d" % num_states)
print("Number of actions: %d" % num_actions)
print("Limit of steps per episode: %d" % steps_limit)
print("-----------------------------------------")
# Instanciate reinforcement learning agent which contains Actor/Critic DNN.
agent = DDPGAgent(env)
# Exploration noise generator which uses Ornstein-Uhlenbeck process.
noise = OUNoise(num_actions)
for i in range(episodes_num):
print("--------Episode %d--------" % i)
reward_per_episode = 0
observation = env.reset()
for j in range(steps_limit):
if is_movie_on: env.render()
# Select action off-policy
state = observation
action = agent.feed_forward_actor(
np.reshape(state, [1, num_states]))
action = action[0] + noise.generate()
# Throw action to environment
observation, reward, done, info = env.step(action)
# For replay buffer. (s_t, a_t, s_t+1, r)
agent.add_experience(state, action, observation, reward, done)
# Train actor/critic network
if len(agent.replay_buffer) > MINI_BATCH_SIZE: agent.train()
reward_per_episode += reward
if (done or j == steps_limit - 1):
print("Steps count: %d" % j)
print("Total reward: %d" % reward_per_episode)
#noise.reset()
with open("reward_log.csv", "a") as f:
f.write("%d,%f\n" % (i, reward_per_episode))
break
