from ddpg import Agent
import gym
import numpy as np
env = gym.make('LunarLanderContinuous-v2')
agent = Agent(alpha = 0.000025, beta = 0.00025, input_dims = [8], tau = 0.001, env = env, batch_size = 64, layer1_size = 400, layer2_size = 300, n_actions = 2)
np.random.seed(42)
score_history = []
for i in range(1000):
done = False
score = 0
obs = env.reset()
while not done:
act = agent.choose_action(obs)
new_state, reward, done, info = env.step(act)
agent.remember(obs, act, reward, new_state, int(done))
agent.learn()
score += reward
obs = new_state
score_history.append(score)
print("Episode - {} Score - {} 100 game average {}".format(i, score, np.mean(score_history[-100:])))
if i % 25 == 0:
agent.save_models()
filename = l
env_params = env.reset()
obs = env_params[
'observation'] # Remove 'observation' indexing for envs with no dict
d_goal = env_params['desired_goal']
net_input = np.hstack((obs, d_goal))
# print(obs.shape)
done = False
score = 0
while not done:
act = agent.choose_action(net_input)
# print(act)
new_state, reward, done, info = env.step(act)
new_state = np.hstack(
(new_state['observation'], new_state['desired_goal']
)) ## Remove 'observation' indexing for envs with no dict
agent.remember(net_input, act, reward, new_state, int(done))
agent.learn()
score += reward
net_input = new_state
#env.render()
score_history.append(score)
if i % 25 == 0:
agent.save_models()
print('episode ', i, 'score %.2f' % score,
'trailing 100 games avg %.3f' % np.mean(score_history[-100:]))
filename = 'LunarLander-alpha000025-beta00025-400-300.png'
plotLearning(score_history, filename, window=100)
base_dir: str = os.path.dirname(__file__)
figure_file = os.path.abspath(os.path.join(base_dir, 'plots/pendulum.png'))
best_score = env.reward_range[0]
score_history = []
load_checkpoint = False
if load_checkpoint:
n_steps = 0
while n_steps <= agent.batch_size:
observation = env.reset()
action = env.action_space.sample()
observation_, reward, done, info = env.step(action)
agent.remember(observation, action, reward, observation_, done)
n_steps += 1
agent.learn()
agent.load_models()
evaluate = True
else:
evaluate = False
for i in range(n_episodes):
observation = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(observation, evaluate)
env.render()
observation_, reward, done, info = env.step(action)
input_dims=[3],
tau=0.001,
env=env,
n_actions=1)
np.random.seed(0)
score_history = []
for episode in range(1000):
state = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(state)
next_state, reward, done, info = env.step(action)
agent.remember(state, action, reward, next_state, int(done))
agent.learn()
score += reward
state = next_state
score_history.append(score)
print('Episode {}, Score: {:.2f}, 100 game average: {:.2f}'.format(
episode, score, np.mean(score_history[-100:])))
filename = 'pendulum.png'
plotLearning(score_history, filename, window=100)
history = []
critic_loss = []
actor_loss = []
for i in range(n_episodes):
loss1 = []
loss2 = []
obs = env.reset()
done = False
score = 0
while not done:
action = agent.choose_action(obs)
obs_, reward, done, _ = env.step(action)
score += reward
agent.remember(obs, action, reward, obs_, done)
agent.learn()
obs = obs_
history.append(score)
avg = np.zeros(len(history))
for i in range(len(avg)):
avg[i] = np.mean(history[max(0, i - 25):(i + 1)])
plt.plot(range(1, n_episodes + 1), avg)
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.title(f'{env_name} Showing Mean Reward')
plt.savefig(f'results/{env_name}-DDPG-Showing-Mean-Rewards-b128.jpg')
plt.show()
