episodes = args.episode
time_steps = args.steps
epsilon = args.epsilon
render = args.render
# --- run algorithm ---
# td_lam.load_models()
stats = td_lam.train(env, episodes, time_steps, epsilon)
# td_lam.save_models()
# --- visualize the results ---
result_folder = Path(__file__).resolve().parent / 'results'
viz = Visualizer(result_path=result_folder)
viz.plot_episode_length(stats,
plot_name='td_episode_length_{}'.format(
args.exp_count))
viz.plot_reward(stats, plot_name='td_rewards_{}'.format(args.exp_count))
# --- animation ---
if render:
with contextlib.closing(ContinuousCartPoleEnv()) as env:
for _ in range(2):
s = env.reset()
for _ in range(300):
env.render()
a = td_lam.get_action(s, epsilon=0.02)
s, _, d, _ = env.step(a)
if d:
break
action_dim,
gamma=0.99,
hidden_dim=hidden_dim,
policy_lr=policy_lr,
baseline_lr=baseline_lr,
d2c=d2c_converter)
# --- run algorithm ---
if load_flag:
reinforce.load_models(model_name=exp_count)
stats = reinforce.train(env=env, episodes=episodes, time_steps=timesteps)
reinforce.save_models(model_name=exp_count)
# --- visualize the results ---
result_folder = Path(__file__).resolve().parent / 'results'
viz = Visualizer(result_path=result_folder)
viz.plot_episode_length(stats, plot_name=f'r_d_episodes_{exp_count}')
viz.plot_reward(stats, plot_name=f'r_d_rewards_{exp_count}')
# --- animation ---
if render_flag:
with contextlib.closing(ContinuousCartPoleEnv()) as env:
for _ in range(2):
s = env.reset()
for _ in range(500):
env.render()
a, _ = reinforce.get_action(s)
s, _, d, _ = env.step(a)
if d:
break
critic_lr=critic_lr,
K_epochs=K_epochs,
eps_clip=eps_clip,
entropy_coeff=entropy_coeff,
verbose=verbose_flag)
if load_flag:
ppo.load_models(model_name=exp_count)
stats = ppo.train(env=env,
episodes=episodes,
timesteps=timesteps,
update_timestep=update_timestep)
ppo.save_models(model_name=exp_count)
# --- visualize the results ---
result_folder = Path(__file__).resolve().parent / 'results'
viz = Visualizer(result_path=result_folder)
viz.plot_episode_length(stats, plot_name=f'ppo_c_episodes_{exp_count}')
viz.plot_reward(stats, plot_name=f'ppo_c_rewards_{exp_count}')
# --- animation ---
if render_flag:
with contextlib.closing(ContinuousCartPoleEnv()) as env:
for _ in range(3):
s = env.reset()
for _ in range(500):
env.render()
a = ppo.get_action(s)
s, _, d, _ = env.step(a)
if d:
break
