!pip install torch norse

import gym
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import norse.torch as norse

# Gradient Echo Layer
class GradientEchoLayer(nn.Module):
    def __init__(self, input_size, output_size, echo_factor=0.9, memory_size=5):
        super(GradientEchoLayer, self).__init__()
        self.fc = nn.Linear(input_size, output_size)
        self.echo_factor = echo_factor
        self.memory_size = memory_size
        self.gradient_memory = []

    def forward(self, x):
        return torch.relu(self.fc(x))

    def modify_grad(self, grad):
        if len(self.gradient_memory) > 0 and grad.shape != self.gradient_memory[0].shape:
            self.gradient_memory = []

        if len(self.gradient_memory) >= self.memory_size:
            self.gradient_memory.pop(0)
        self.gradient_memory.append(grad.clone().detach())

        echo_grad = torch.zeros_like(grad)
        for i, past_grad in enumerate(reversed(self.gradient_memory)):
            echo_grad += self.echo_factor ** i * past_grad

        return grad + echo_grad

    def register_hooks(self):
        self.fc.weight.register_hook(self.modify_grad)
        self.fc.bias.register_hook(self.modify_grad)

# Policy Network with Gradient Echo
class PolicyNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(PolicyNetwork, self).__init__()
        self.layer1 = GradientEchoLayer(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

        self.layer1.register_hooks()

    def forward(self, x):
        x = self.layer1(x)
        x = torch.relu(self.fc2(x))
        return torch.softmax(self.fc3(x), dim=-1)

# Liquid State Machine (LSM) for reward processing
class LiquidStateLayer(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(LiquidStateLayer, self).__init__()
        self.lif = norse.LIFCell()  # Leaky Integrate-and-Fire neuron model
        self.fc = nn.Linear(input_size, hidden_size)
    
    def forward(self, x, state=None):
        x = self.fc(x)
        out, state = self.lif(x, state)
        return out, state

# Surrogate Model with LSM and Gradient Echo for reward prediction
class SurrogateModelWithLSM(nn.Module):
    def __init__(self, input_size, liquid_hidden_size, hidden_size):
        super(SurrogateModelWithLSM, self).__init__()
        self.liquid_state_layer = LiquidStateLayer(input_size, liquid_hidden_size)
        self.gradient_echo_layer = GradientEchoLayer(liquid_hidden_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, 1)

        self.gradient_echo_layer.register_hooks()

    def forward(self, x, symbolic_output):
        state = None  # Initialize state for LIF neurons
        liquid_out, state = self.liquid_state_layer(x, state)
        x = torch.relu(self.gradient_echo_layer(liquid_out))
        
        # Reward adjustment based on symbolic reasoning
        reward = self.fc2(x)
        if symbolic_output['risk'] == "freeze":
            reward -= 1
        elif symbolic_output['risk'] == "dry_out":
            reward -= 0.5
            
        return reward

# Simple Neural Network for symbolic reasoning
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 2)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        return torch.sigmoid(self.fc3(x))  # Output between 0 and 1 for binary decision

# Symbolic reasoning based on environmental factors
def symbolic_reasoning(rain, sprinkler, temperature):
    wet = rain or sprinkler
    if temperature < 0:
        risk = "freeze"
    elif temperature > 30:
        risk = "dry_out"
    else:
        risk = "normal"
    return {"wet": wet, "risk": risk}

# Decision making based on neural and symbolic outputs
def hybrid_decision_making(nn_output, symbolic_output):
    nn_output = nn_output.squeeze()
    if symbolic_output['risk'] == "freeze" and nn_output[0] > nn_output[1]:
        return "Action A"
    elif symbolic_output['risk'] == "dry_out" and nn_output[1] > nn_output[0]:
        return "Action B"
    return "Action B" if symbolic_output['wet'] else "Action A"

# Action selection function
def select_action(state, symbolic_output, surrogate_model):
    state_tensor = torch.tensor(state, dtype=torch.float32)
    action_probs = policy_net(state_tensor)
    action = torch.multinomial(action_probs, 1).item()
    log_prob = torch.log(action_probs[action])
    symbolic_decision = hybrid_decision_making(action_probs, symbolic_output)
    predicted_reward = surrogate_model(state_tensor, symbolic_output)
    
    return action, log_prob, symbolic_decision, predicted_reward

# Meta-learning update function
def meta_learning_update(policy_net, optimizer, rewards, log_probs, surrogate_model, surrogate_optimizer, states, symbolic_outputs, discount_factor=0.99):
    discounted_rewards = []
    for t in range(len(rewards)):
        Gt = sum([r * (discount_factor ** i) for i, r in enumerate(rewards[t:])])
        discounted_rewards.append(Gt)
    discounted_rewards = torch.tensor(discounted_rewards)
    discounted_rewards = (discounted_rewards - discounted_rewards.mean()) / (discounted_rewards.std() + 1e-9)  # Normalize rewards

    loss = -sum([log_prob * Gt for log_prob, Gt in zip(log_probs, discounted_rewards)])

    optimizer.zero_grad()
    loss.backward()
    torch.nn.utils.clip_grad_norm_(policy_net.parameters(), 1.0)  # Gradient clipping
    optimizer.step()

    # Update the surrogate model using the true rewards and states
    surrogate_optimizer.zero_grad()
    surrogate_loss = 0
    for state, symbolic_output, Gt in zip(states, symbolic_outputs, discounted_rewards):
        surrogate_loss += ((surrogate_model(state, symbolic_output) - Gt) ** 2).mean()
    surrogate_loss.backward()
    surrogate_optimizer.step()

# Set up the environment using the new step API
env = gym.make('CartPole-v1', new_step_api=True)

# Initialize Policy Network and Surrogate Model with LSM for reward prediction
policy_net = PolicyNetwork(input_size=env.observation_space.shape[0],
                           hidden_size=128,
                           output_size=env.action_space.n)

surrogate_model = SurrogateModelWithLSM(input_size=env.observation_space.shape[0],
                                        liquid_hidden_size=128,
                                        hidden_size=128)

# Initialize the Simple Neural Network for symbolic reasoning
simple_nn = SimpleNN()

optimizer = optim.Adam(policy_net.parameters(), lr=0.001)
surrogate_optimizer = optim.Adam(surrogate_model.parameters(), lr=0.001)

# Run the training loop
num_episodes = 3000
for episode in range(num_episodes):
    state = env.reset()
    done = False
    rewards = []
    log_probs = []
    states = []
    symbolic_outputs = []

    while not done:
        # Generate random input for the symbolic NN
        symbolic_input = torch.randn(1, 10)
        symbolic_nn_output = simple_nn(symbolic_input)
        temperature = np.random.uniform(-5, 40)  # Random temperature input
        symbolic_output = symbolic_reasoning(rain=True, sprinkler=False, temperature=temperature)

        action, log_prob, symbolic_decision, predicted_reward = select_action(state, symbolic_output, surrogate_model)
        state, reward, done, truncated, _ = env.step(action)
        rewards.append(reward)
        log_probs.append(log_prob)
        states.append(torch.tensor(state, dtype=torch.float32))
        symbolic_outputs.append(symbolic_output)

        if done or truncated:
            meta_learning_update(policy_net, optimizer, rewards, log_probs, surrogate_model, surrogate_optimizer, torch.stack(states), symbolic_outputs)
            total_reward = sum(rewards)
            if episode % 100 == 0:
                print(f"Episode {episode}, Total Reward: {total_reward}, Symbolic Decision: {symbolic_decision}")
            break

env.close()


      
# Copyright 2024 square play

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.