From 3b582a8d9de216481b4ac31f406d061989699b7b Mon Sep 17 00:00:00 2001
From: Majdi Karim <karim.majdi@etu.ec-lyon.fr>
Date: Tue, 5 Mar 2024 21:30:16 +0000
Subject: [PATCH] Add new file
---
a2c_sb3_cartpole.py | 93 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 93 insertions(+)
create mode 100644 a2c_sb3_cartpole.py
diff --git a/a2c_sb3_cartpole.py b/a2c_sb3_cartpole.py
new file mode 100644
index 0000000..a767524
--- /dev/null
+++ b/a2c_sb3_cartpole.py
@@ -0,0 +1,93 @@
+import gymnasium as gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.distributions import Categorical
+import matplotlib.pyplot as plt
+
+
+
+
+
+
+
+
+
+
+# Create the environment
+env = gym.make("CartPole-v1", render_mode="human")
+
+# Reset the environment and get the initial observation
+observation = env.reset()
+
+state_size = env.observation_space.shape[0]
+action_size = env.action_space.n
+# Define the agent neural network model
+class Policy(nn.Module):
+ def __init__(self, state_size, action_size, hidden_size=128):
+ super(Policy, self).__init__()
+ self.fc1 = nn.Linear(state_size, hidden_size)
+ self.relu = nn.ReLU()
+ self.dropout = nn.Dropout(p=0.6) # Adjust dropout probability as needed
+ self.fc2 = nn.Linear(hidden_size, action_size)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.relu(x)
+ x = self.dropout(x)
+ x = self.fc2(x)
+ return F.softmax(x)
+
+policy_model = Policy(state_size, action_size)
+optimizer = optim.Adam(policy_model.parameters(), lr=5e-3)
+
+gamma = 0.99
+episodes_rewards = []
+
+for i in range(500):
+ # Reset the environment
+ # init buffers
+ observation, info = env.reset(seed=42)
+ episode_rewards = []
+ logarithmich_probabilities = []
+ terminated = False
+ # Render the environment to visualize the agent's behavior
+ env.render()
+
+ while terminated == False:
+ # Get action probabilities from the policy model
+ action_probabilities = policy_model(torch.tensor(observation, dtype=torch.float32))
+ action_distribution = Categorical(action_probabilities)
+
+ # Sample an action from the action distribution
+ action = action_distribution.sample()
+ logarithmich_probability = action_distribution.log_prob(action)
+ logarithmich_probabilities.append(logarithmich_probability)
+ print(int(action.item()))
+ # Take a step in the environment
+ #print(env.step(action.item()))
+ next_observation, reward, done, a, b = env.step(action.item())
+ episode_rewards.append(reward)
+
+ # Update observation
+ observation = next_observation
+
+
+ # Compute the return for the episode
+ returns = []
+ R = 0
+ for r in reversed(episode_rewards):
+ R = r + gamma * R
+ returns.insert(0, R)
+
+ # Compute the policy loss
+ policy_loss = torch.tensor([-loga_prob * R for loga_prob, R in zip(logarithmich_probabilities, returns)]).sum()
+ episodes_rewards += [-policy_loss]
+ # Update the policy model
+ optimizer.zero_grad()
+ policy_loss.backward()
+ optimizer.step()
+
+
+env.close()
--
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