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Add analysis plotly #1

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8 changes: 8 additions & 0 deletions PPO.html
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29 changes: 25 additions & 4 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,17 @@
from replay_memory import Memory
from running_state import ZFilter

import plotly
import plotly.graph_objs as go
from plotly.graph_objs import Layout,Scatter

PI = torch.DoubleTensor([3.1415926])

def normal_log_density(x, mean, log_std, std):
var = std.pow(2)
log_density = -(x - mean).pow(2) / (2 * var) - 0.5 * torch.log(2 * Variable(PI)) - log_std
return log_density.sum(1)

def select_action(state, policy_net):
torch.set_default_tensor_type('torch.DoubleTensor')
PI = torch.DoubleTensor([3.1415926])
Expand Down Expand Up @@ -128,7 +139,7 @@ def main(gamma=0.995, env_name='Walker2d-v2', tau=0.97, seed=543, number_of_batc
running_state = ZFilter((num_inputs,), clip=5)
running_reward = ZFilter((1,), demean=False, clip=10)
episode_lengths = []

plot_rew = []
for i_episode in range(number_of_batches):
memory = Memory()

Expand Down Expand Up @@ -166,11 +177,21 @@ def main(gamma=0.995, env_name='Walker2d-v2', tau=0.97, seed=543, number_of_batc

reward_batch /= num_episodes
batch = memory.sample()
plot_rew.append(reward_batch)
update_params(batch, policy_net, value_net, gamma, opt_policy, opt_value)

if i_episode % args.log_interval == 0:
print('Episode {}\tLast reward: {}\tAverage reward {:.2f}'.format(
i_episode, reward_sum, reward_batch))

plot_epi = []
for i in range (number_of_batches):
plot_epi.append(i)
trace = go.Scatter( x = plot_epi, y = plot_rew)
layout = go.Layout(title='PPO',xaxis=dict(title='Episodes', titlefont=dict(family='Courier New, monospace',size=18,color='#7f7f7f')),
yaxis=dict(title='Average Reward', titlefont=dict(family='Courier New, monospace',size=18,color='#7f7f7f')))

plotly.offline.plot({"data": [trace], "layout": layout},filename='PPO.html',image='jpeg')

if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch actor-critic example')
Expand All @@ -188,11 +209,11 @@ def main(gamma=0.995, env_name='Walker2d-v2', tau=0.97, seed=543, number_of_batc
# help='damping (default: 1e-1)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
help='random seed (default: 1)')
parser.add_argument('--number-of-batches', type=int, default=500, metavar='N',
parser.add_argument('--number-of-batches', type=int, default=50, metavar='N',
help='number of batches (default: 500)')
parser.add_argument('--batch-size', type=int, default=5000, metavar='N',
parser.add_argument('--batch-size', type=int, default=20, metavar='N',
help='batch size (default: 5000)')
parser.add_argument('--maximum_steps', type=int, default=10000, metavar='N',
parser.add_argument('--maximum_steps', type=int, default=10, metavar='N',
help='maximum number of steps (default: 10000)')
parser.add_argument('--render', action='store_true',
help='render the environment')
Expand Down