def run_watermaze(num_agents, params, lesion_hpc=False, **kwargs):
g = HexWaterMaze(6)
for i_a in tqdm(range(num_agents), desc='Agent'):
if lesion_hpc:
filename = 'spatial_agent{}_lesion'.format(i_a)
elif 'inact_hpc' in kwargs:
filename = 'spatial_partial_lesion_agent{}'.format(i_a)
else:
filename = 'spatial_agent{}'.format(i_a)
if os.path.exists(os.path.join(res_dir, filename)):
tqdm.write('Already done')
continue
#possible_platform_states = np.array([192, 185, 181, 174, 216, 210, 203, 197]) # for the r = 10 case
possible_platform_states = np.array([48, 45, 42, 39, 60, 57, 54, 51])
platform_sequence = determine_platform_seq(possible_platform_states, g)
# intialise agent
agent = SpatialAgent(g,
init_sr='rw',
A_alpha=params['A_alpha'][i_a],
alpha1=params['alpha1'][i_a],
A_beta=params['A_beta'][i_a],
beta1=params['beta1'][i_a],
lesion_hpc=lesion_hpc,
inact_hpc=kwargs['inact_hpc'][i_a])
agent_results = []
total_trial_count = 0
for ses in tqdm(range(11), desc='Session', leave=False):
for trial in tqdm(range(4), leave=False, desc='Trial'):
# every first trial of a session, change the platform location
if trial == 0:
g.set_platform_state(platform_sequence[ses])
res = agent.one_episode(random_policy=False)
res['trial'] = trial
res['escape time'] = res.time.max()
res['session'] = ses
res['total trial'] = total_trial_count
agent_results.append(res)
total_trial_count += 1
agent_df = pd.concat(agent_results)
agent_df['total time'] = np.arange(len(agent_df))
agent_df.to_csv(os.path.join(res_dir, filename))
import numpy as np
import matplotlib.pyplot as plt
from hippocampus.agents import LandmarkCells
from hippocampus.utils import angle_to_landmark
from hippocampus.environments import HexWaterMaze
g = HexWaterMaze(6)
location = g.grid.cart_coords[g.platform_state]
my_location = (0, 0)
my_orientation = 60
angle = angle_to_landmark(my_location, location, my_orientation)
LC = LandmarkCells()
xs = np.linspace(-180, 180, 1000)
responses = np.zeros((len(xs), LC.n_cells))
for i, x in enumerate(xs):
responses[i, :] = LC.compute_response(i)
for col in responses.T:
plt.plot(xs, col)
plt.show()
figure_folder = os.path.join(results_folder, 'figures')
if not os.path.exists(results_folder):
os.makedirs(results_folder)
os.makedirs(figure_folder)
params = pd.DataFrame({
'n_agents': [n_agents],
'inv_temp': [inv_temp],
'gamma': [gamma],
'lesion HPC': [lesion_hippocampus],
'lesion DLS': [lesion_striatum]
})
params.to_csv(os.path.join(results_folder, 'params.csv'))
# initialise environment
g = HexWaterMaze(10)
# determine platform sequence
possible_platform_states = np.array([192, 185, 181, 174, 216, 210, 203,
197]) # for the r = 10 case
def determine_platform_seq(platform_states):
indices = np.arange(len(platform_states))
usage = np.zeros(len(platform_states))
plat_seq = [np.random.choice(platform_states)]
for sess in range(1, 11):
distances = np.array(
[g.grid.distance(plat_seq[sess - 1], s) for s in platform_states])
candidates = indices[np.logical_and(usage < 2,
'Control - trial 4'])
if not op.exists(figure_location):
os.makedirs(figure_location)
plt.savefig(os.path.join(figure_location, 'pearce_escapetime_firstlast'), format='pdf')
plt.show()
plt.close()
if __name__ == '__main__':
from hippocampus.environments import HexWaterMaze
# create_summary_file(data_directories['lesion'])
plot_escape_time()
maze = HexWaterMaze(10)
# pick example agent
for group in ['control', 'lesion']:
agents = {'control': 89, 'lesion': 12}
session = 6
df = pd.read_csv(op.join(data_directories[group], 'agent{}.csv'.format(agents[group])))
s6t0 = df[np.logical_and(df.trial == 0, df.session == session)]
previous_platform = df[df.session == session - 1].platform.iloc[0]
s6t0['previous platform'] = previous_platform
current_platform = s6t0.platform.iloc[0]
maze.plot_occupancy_on_grid(s6t0, alpha=1., show_state_idx=False)
inv_temp = 5.
gamma = .99
lesion_hippocampus = True
lesion_striatum = False
params = pd.DataFrame({
'n_agents': [n_agents],
'inv_temp': [inv_temp],
'gamma': [gamma],
'lesion HPC': [lesion_hippocampus],
'lesion DLS': [lesion_striatum]
})
params.to_csv(os.path.join(results_folder, 'params.csv'))
# initialise environment
g = HexWaterMaze(10)
# determine platform sequence
possible_platform_states = np.array([192, 185, 181, 174, 216, 210, 203,
197]) # for the r = 10 case
def determine_platform_seq(platform_states):
indices = np.arange(len(platform_states))
usage = np.zeros(len(platform_states))
plat_seq = [np.random.choice(platform_states)]
for sess in range(1, 11):
distances = np.array(
[g.grid.distance(plat_seq[sess - 1], s) for s in platform_states])
candidates = indices[np.logical_and(usage < 2,
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from hippocampus.agents import LandmarkLearningAgent, CombinedAgent
from hippocampus.environments import HexWaterMaze
if __name__ == '__main__':
from tqdm import tqdm
from hippocampus.plotting import tsplot_boot
g = HexWaterMaze(5)
g.set_platform_state(30)
all_ets = []
for ag in tqdm(range(5)):
agent = LandmarkLearningAgent(g)
agent_results = []
agent_ets = []
for ep in range(60):
res = agent.one_episode()
res['trial'] = ep
res['escape time'] = res.time.max()
agent_results.append(res)
agent_ets.append(res.time.max())
all_ets.append(agent_ets)
fig, ax = plt.subplots()
tsplot_boot(ax, np.array(all_ets))
plt.show()