Agent Conversation: deepseek-reasoner
Model: deepseek-reasoner
Total steps: 8
Messages: 17
UserStep 0
Task: rl-offpolicy-continuous
Online RL: Off-Policy Actor-Critic for Continuous Control
Objective
Design and implement an off-policy actor-critic RL algorithm for continuous control. Your code goes in custom_offpolicy_continuous.py. Three reference implementations (DDPG, TD3, SAC) are provided as read-only.
Background
Off-policy methods maintain a replay buffer of past experience and update the policy using data collected under previous policies. Key challenges include overestimation bias in Q-value estimates, exploration-exploitation tradeoff, and sample efficiency. Different approaches address these through twin critics, entropy regularization, target smoothing, or delayed updates.
Constraints
- Network architecture dimensions are FIXED and cannot be modified
- Total parameter count is enforced at runtime
- Focus on algorithmic innovation: new loss functions, update rules, exploration strategies, etc.
- Do NOT simply copy a reference implementation with minor changes
Evaluation
Trained and evaluated on HalfCheetah-v4, Hopper-v4, Walker2d-v4. Additional held-out environments (not shown during intermediate testing) are used to assess generalization. Metric: mean episodic return over 10 evaluation episodes (higher is better).
cleanrl/cleanrl/custom_offpolicy_continuous.py [EDITABLE — lines 153–244 only]
1: # Custom off-policy continuous RL algorithm for MLS-Bench
2: #
3: # EDITABLE section: Actor, QNetwork, and OffPolicyAlgorithm classes.
4: # FIXED sections: everything else (config, env, buffer, eval, training loop).
5: import os
6: import random
7: import time
8: from dataclasses import dataclass
9:
10: import gymnasium as gym
11: import numpy as np
12: import torch
13: import torch.nn as nn
14: import torch.nn.functional as F
15: import torch.optim as optim
16: import tyro
17:
18:
19: # =====================================================================
20: # FIXED: Configuration
21: # =====================================================================
22: @dataclass
23: class Args:
24: exp_name: str = os.path.basename(__file__)[: -len(".py")]
25: """the name of this experiment"""
26: seed: int = 1
27: """seed of the experiment"""
28: torch_deterministic: bool = True
29: """if toggled, `torch.backends.cudnn.deterministic=False`"""
30: cuda: bool = True
31: """if toggled, cuda will be enabled by default"""
32:
33: # Algorithm specific arguments
34: env_id: str = "HalfCheetah-v4"
35: """the id of the environment"""
36: total_timesteps: int = 1000000
37: """total timesteps of the experiments"""
38: learning_rate: float = 3e-4
39: """the learning rate of the optimizer"""
40: buffer_size: int = int(1e6)
41: """the replay memory buffer size"""
42: gamma: floaAssistantStep 1edit
Tool Input
1{
2 "op": "replace",
3 "filename": "cleanrl/cleanrl/custom_offpolicy_continuous.py",
4 "start_line": 153,
5 "end_line": 244,
6 "content": "class Actor(nn.Module):\n \"\"\"Actor network. Must implement forward(obs) and get_action(obs).\n\n forward(obs) -> action tensor (used for training).\n get_action(obs) -> action tensor (used for evaluation, no grad).\n \"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action):\n super().__init__()\n self.max_action = max_action\n self.fc1 = nn.Linear(obs_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc_mu = nn.Linear(256, action_dim)\n self.register_buffer(\"action_scale\", torch.tensor(max_action, dtype=torch.float32))\n\n def forward(self, obs):\n x = F.relu(self.fc1(obs))\n x = F.relu(self.fc2(x))\n return torch.tanh(self.fc_mu(x)) * self.action_scale\n\n @torch.no_grad()\n def get_action(self, obs):\n return self.forward(obs)\n\n\nclass QNetwork(nn.Module):\n \"\"\"Q-function Q(s, a) -> scalar.\"\"\"\n\n def __init__(self, obs_dim, action_dim):\n super().__init__()\n self.fc1 = nn.Linear(obs_dim + action_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc3 = nn.Linear(256, 1)\n\n def forward(self, obs, action):\n x = torch.cat([obs, action], dim=-1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n return self.fc3(x)\n\n\nclass OffPolicyAlgorithm:\n \"\"\"TD3-CQL: Twin Delayed Deep Deterministic Policy Gradient with Conservative Q-Learning regularization.\"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action, device, args):\n self.device = device\n self.max_action = max_action\n self.gamma = args.gamma\n self.tau = args.tau\n self.exploration_noise = args.exploration_noise\n self.policy_frequency = args.policy_frequency\n self.policy_noise = 0.2\n self.noise_clip = 0.5\n self.total_it = 0\n # Conservative Q-learning hyperparameter\n self.cql_alpha = 0.5 # regularization weight\n self.num_random_actions = 10 # number of random actions for logsumexp\n\n # Actor and target\n self.actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor.load_state_dict(self.actor.state_dict())\n\n # Twin critics and targets\n self.qf1 = QNetwork(obs_dim, action_dim).to(device)\n self.qf2 = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf2_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target.load_state_dict(self.qf1.state_dict())\n self.qf2_target.load_state_dict(self.qf2.state_dict())\n\n self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=args.learning_rate)\n self.q_optimizer = optim.Adam(\n list(self.qf1.parameters()) + list(self.qf2.parameters()),\n lr=args.learning_rate,\n )\n\n def select_action(self, obs):\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n with torch.no_grad():\n action = self.actor(obs_t).cpu().numpy().flatten()\n noise = np.random.normal(0, self.max_action * self.exploration_noise, size=action.shape)\n return np.clip(action + noise, -self.max_action, self.max_action)\n\n def update(self, batch):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n batch_size = obs.shape[0]\n\n with torch.no_grad():\n # Target policy smoothing\n noise = (torch.randn_like(actions) * self.policy_noise).clamp(\n -self.noise_clip, self.noise_clip\n ) * self.max_action\n next_actions = (self.target_actor(next_obs) + noise).clamp(\n -self.max_action, self.max_action\n )\n target_q1 = self.qf1_target(next_obs, next_actions).view(-1)\n target_q2 = self.qf2_target(next_obs, next_actions).view(-1)\n target_q = torch.min(target_q1, target_q2)\n td_target = rewards + (1 - dones) * self.gamma * target_q\n\n # Current Q estimates\n q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n\n # TD loss\n critic_loss = F.mse_loss(q1, td_target) + F.mse_loss(q2, td_target)\n\n # Conservative Q-learning regularization\n # Sample random actions uniformly from action space\n random_actions = torch.rand((batch_size * self.num_random_actions, actions.shape[-1]), device=self.device) * 2 - 1 # uniform [-1,1]\n random_actions = random_actions * self.max_action # scale to environment range\n # Repeat observations for each random action\n repeated_obs = obs.repeat_interleave(self.num_random_actions, dim=0)\n # Compute Q-values for random actions\n q1_rand = self.qf1(repeated_obs, random_actions).view(batch_size, self.num_random_actions)\n q2_rand = self.qf2(repeated_obs, random_actions).view(batch_size, self.num_random_actions)\n # logsumexp over random actions (approximate max Q)\n cql_term1 = torch.logsumexp(q1_rand, dim=1) - q1\n cql_term2 = torch.logsumexp(q2_rand, dim=1) - q2\n cql_loss = (cql_term1.mean() + cql_term2.mean()) * self.cql_alpha\n\n total_critic_loss = critic_loss + cql_loss\n\n self.q_optimizer.zero_grad()\n total_critic_loss.backward()\n self.q_optimizer.step()\n\n actor_loss_val = 0.0\n if self.total_it % self.policy_frequency == 0:\n # Actor loss: maximize Q1 (or both)\n actor_actions = self.actor(obs)\n q1_pi = self.qf1(obs, actor_actions).view(-1)\n actor_loss = -q1_pi.mean()\n self.actor_optimizer.zero_grad()\n actor_loss.backward()\n self.actor_optimizer.step()\n actor_loss_val = actor_loss.item()\n\n # Soft update target networks\n soft_update(self.target_actor, self.actor, self.tau)\n soft_update(self.qf1_target, self.qf1, self.tau)\n soft_update(self.qf2_target, self.qf2, self.tau)\n\n return {\"critic_loss\": critic_loss.item(), \"cql_loss\": cql_loss.item(), \"actor_loss\": actor_loss_val}"
7}Tool ResultStep 1
OK: Replaced lines 153..244 with 147 line(s) in cleanrl/cleanrl/custom_offpolicy_continuous.py. Editable range: 153–299. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–299 | total: 380 lines] 150| # ===================================================================== 151| # EDITABLE: Network definitions and OffPolicyAlgorithm 152| # ===================================================================== 153 class Actor(nn.Module): 154 """Actor network. Must implement forward(obs) and get_action(obs). 155 156 forward(obs) -> action tensor (used for training). 157 get_action(obs) -> action tensor (used for evaluation, no grad). 158 """ 159 160 def __init__(self, obs_dim, action_dim, max_action): 161 super().__init__() 162 self.max_action = max_action 163 self.fc1 = nn.Linear(obs_dim, 256) 164 self.fc2 = nn.Linear(256, 256) 165 self.fc_m
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[Test #1] (2 tests remaining; call submit(n=1) to submit this result) ## Seed 42 ### halfcheetah-v4 (scripts/halfcheetah.sh) Training metrics (last steps): TRAIN_METRICS step=195000 critic_loss=480158.5000 cql_loss=-505.9636 actor_loss=-847.3933 TRAIN_METRICS step=196000 critic_loss=1448428.0000 cql_loss=-972.1432 actor_loss=-702.2694 TRAIN_METRICS step=197000 critic_loss=363877.0625 cql_loss=-652.3666 actor_loss=-760.8384 TRAIN_METRICS step=198000 critic_loss=821577.3750 cql_loss=-248.1454 actor_loss=-747.8562 TRAIN_METRICS step=199000 critic_loss=553436.6250 cql_loss=-715.7583 actor_loss=-760.5996 Evaluation (halfcheetah-v4): Eval episodic_return: -697.12 Eval episodic_return: -836.80 Eval episodic_return: -685.41 Final eval return: -685.41 ### reacher-v4 (scripts/reacher.sh) Training metrics (last steps): TRAIN_METRICS step=215000 critic_loss=2.0794 cql_loss=0.8777 actor_loss=63.8116 TRAIN_METRICS step=216000 critic_loss=1.7323 cql_loss=0.8234 actor_loss=63.9000 TRAIN_METRICS st
AssistantStep 3edit
Tool Input
1{
2 "op": "replace",
3 "filename": "cleanrl/cleanrl/custom_offpolicy_continuous.py",
4 "start_line": 153,
5 "end_line": 244,
6 "content": "class Actor(nn.Module):\n \"\"\"Actor network. Must implement forward(obs) and get_action(obs).\n\n forward(obs) -> action tensor (used for training).\n get_action(obs) -> action tensor (used for evaluation, no grad).\n \"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action):\n super().__init__()\n self.max_action = max_action\n self.fc1 = nn.Linear(obs_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc_mu = nn.Linear(256, action_dim)\n self.register_buffer(\"action_scale\", torch.tensor(max_action, dtype=torch.float32))\n\n def forward(self, obs):\n x = F.relu(self.fc1(obs))\n x = F.relu(self.fc2(x))\n return torch.tanh(self.fc_mu(x)) * self.action_scale\n\n @torch.no_grad()\n def get_action(self, obs):\n return self.forward(obs)\n\n\nclass QNetwork(nn.Module):\n \"\"\"Q-function Q(s, a) -> scalar.\"\"\"\n\n def __init__(self, obs_dim, action_dim):\n super().__init__()\n self.fc1 = nn.Linear(obs_dim + action_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc3 = nn.Linear(256, 1)\n\n def forward(self, obs, action):\n x = torch.cat([obs, action], dim=-1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n return self.fc3(x)\n\n\nclass OffPolicyAlgorithm:\n \"\"\"TD3-UE: Twin Delayed Deep Deterministic Policy Gradient with Uncertainty-Driven Exploration.\"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action, device, args):\n self.device = device\n self.max_action = max_action\n self.gamma = args.gamma\n self.tau = args.tau\n self.exploration_noise = args.exploration_noise\n self.policy_frequency = args.policy_frequency\n self.policy_noise = 0.2\n self.noise_clip = 0.5\n self.total_it = 0\n # Uncertainty-driven exploration hyperparameter\n self.beta = 0.1 # scaling factor for Q uncertainty\n self.min_noise_scale = 0.5 # minimum noise scaling\n self.max_noise_scale = 2.0 # maximum noise scaling\n\n # Actor and target\n self.actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor.load_state_dict(self.actor.state_dict())\n\n # Twin critics and targets\n self.qf1 = QNetwork(obs_dim, action_dim).to(device)\n self.qf2 = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf2_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target.load_state_dict(self.qf1.state_dict())\n self.qf2_target.load_state_dict(self.qf2.state_dict())\n\n self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=args.learning_rate)\n self.q_optimizer = optim.Adam(\n list(self.qf1.parameters()) + list(self.qf2.parameters()),\n lr=args.learning_rate,\n )\n\n def select_action(self, obs):\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n with torch.no_grad():\n action = self.actor(obs_t)\n # Compute Q-values for this action (no noise) to estimate uncertainty\n q1 = self.qf1(obs_t, action).item()\n q2 = self.qf2(obs_t, action).item()\n q_std = abs(q1 - q2)\n # Adaptive noise scaling based on Q uncertainty\n noise_scale = self.exploration_noise * (1.0 + self.beta * q_std)\n noise_scale = np.clip(noise_scale, self.min_noise_scale, self.max_noise_scale)\n action = action.cpu().numpy().flatten()\n noise = np.random.normal(0, self.max_action * noise_scale, size=action.shape)\n return np.clip(action + noise, -self.max_action, self.max_action)\n\n def update(self, batch):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n with torch.no_grad():\n # Target policy smoothing\n noise = (torch.randn_like(actions) * self.policy_noise).clamp(\n -self.noise_clip, self.noise_clip\n ) * self.max_action\n next_actions = (self.target_actor(next_obs) + noise).clamp(\n -self.max_action, self.max_action\n )\n target_q1 = self.qf1_target(next_obs, next_actions).view(-1)\n target_q2 = self.qf2_target(next_obs, next_actions).view(-1)\n target_q = torch.min(target_q1, target_q2)\n td_target = rewards + (1 - dones) * self.gamma * target_q\n\n # Current Q estimates\n q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n\n # TD loss\n critic_loss = F.mse_loss(q1, td_target) + F.mse_loss(q2, td_target)\n\n # Optional: consensus loss to reduce disagreement between Q networks\n consensus_loss = F.mse_loss(q1, q2) * 0.1 # small weight\n total_critic_loss = critic_loss + consensus_loss\n\n self.q_optimizer.zero_grad()\n total_critic_loss.backward()\n self.q_optimizer.step()\n\n actor_loss_val = 0.0\n if self.total_it % self.policy_frequency == 0:\n # Actor loss: maximize Q1 (or both)\n actor_actions = self.actor(obs)\n q1_pi = self.qf1(obs, actor_actions).view(-1)\n actor_loss = -q1_pi.mean()\n self.actor_optimizer.zero_grad()\n actor_loss.backward()\n self.actor_optimizer.step()\n actor_loss_val = actor_loss.item()\n\n # Soft update target networks\n soft_update(self.target_actor, self.actor, self.tau)\n soft_update(self.qf1_target, self.qf1, self.tau)\n soft_update(self.qf2_target, self.qf2, self.tau)\n\n return {\"critic_loss\": critic_loss.item(), \"consensus_loss\": consensus_loss.item(), \"actor_loss\": actor_loss_val}"
7}Tool ResultStep 3
OK: Replaced lines 153..244 with 142 line(s) in cleanrl/cleanrl/custom_offpolicy_continuous.py. Editable range: 153–349. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–349 | total: 430 lines] 150| # ===================================================================== 151| # EDITABLE: Network definitions and OffPolicyAlgorithm 152| # ===================================================================== 153 class Actor(nn.Module): 154 """Actor network. Must implement forward(obs) and get_action(obs). 155 156 forward(obs) -> action tensor (used for training). 157 get_action(obs) -> action tensor (used for evaluation, no grad). 158 """ 159 160 def __init__(self, obs_dim, action_dim, max_action): 161 super().__init__() 162 self.max_action = max_action 163 self.fc1 = nn.Linear(obs_dim, 256) 164 self.fc2 = nn.Linear(256, 256) 165 self.fc_m
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1{
2 "n": 1
3}Tool ResultStep 4
Undo complete: Restored: cleanrl/cleanrl/custom_offpolicy_continuous.py
AssistantStep 5edit
Tool Input
1{
2 "op": "replace",
3 "filename": "cleanrl/cleanrl/custom_offpolicy_continuous.py",
4 "start_line": 153,
5 "end_line": 299,
6 "content": "class Actor(nn.Module):\n \"\"\"Actor network. Must implement forward(obs) and get_action(obs).\n\n forward(obs) -> action tensor (used for training).\n get_action(obs) -> action tensor (used for evaluation, no grad).\n \"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action):\n super().__init__()\n self.max_action = max_action\n self.fc1 = nn.Linear(obs_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc_mu = nn.Linear(256, action_dim)\n self.register_buffer(\"action_scale\", torch.tensor(max_action, dtype=torch.float32))\n\n def forward(self, obs):\n x = F.relu(self.fc1(obs))\n x = F.relu(self.fc2(x))\n return torch.tanh(self.fc_mu(x)) * self.action_scale\n\n @torch.no_grad()\n def get_action(self, obs):\n return self.forward(obs)\n\n\nclass QNetwork(nn.Module):\n \"\"\"Q-function Q(s, a) -> scalar.\"\"\"\n\n def __init__(self, obs_dim, action_dim):\n super().__init__()\n self.fc1 = nn.Linear(obs_dim + action_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc3 = nn.Linear(256, 1)\n\n def forward(self, obs, action):\n x = torch.cat([obs, action], dim=-1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n return self.fc3(x)\n\n\nclass OffPolicyAlgorithm:\n \"\"\"TD3-GP: Twin Delayed Deep Deterministic Policy Gradient with Gradient Penalty regularization.\"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action, device, args):\n self.device = device\n self.max_action = max_action\n self.gamma = args.gamma\n self.tau = args.tau\n self.exploration_noise = args.exploration_noise\n self.policy_frequency = args.policy_frequency\n self.policy_noise = 0.2\n self.noise_clip = 0.5\n self.total_it = 0\n # Gradient penalty hyperparameter\n self.gp_lambda = 0.1\n\n # Actor and target\n self.actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor.load_state_dict(self.actor.state_dict())\n\n # Twin critics and targets\n self.qf1 = QNetwork(obs_dim, action_dim).to(device)\n self.qf2 = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf2_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target.load_state_dict(self.qf1.state_dict())\n self.qf2_target.load_state_dict(self.qf2.state_dict())\n\n self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=args.learning_rate)\n self.q_optimizer = optim.Adam(\n list(self.qf1.parameters()) + list(self.qf2.parameters()),\n lr=args.learning_rate,\n )\n\n def select_action(self, obs):\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n with torch.no_grad():\n action = self.actor(obs_t).cpu().numpy().flatten()\n noise = np.random.normal(0, self.max_action * self.exploration_noise, size=action.shape)\n return np.clip(action + noise, -self.max_action, self.max_action)\n\n def compute_gradient_penalty(self, obs, actions):\n \"\"\"Compute gradient penalty for critic networks (WGAN-GP style).\"\"\"\n # Random interpolation between real actions and policy actions\n actor_actions = self.actor(obs)\n epsilon = torch.rand(obs.size(0), 1, device=self.device)\n interpolated_actions = epsilon * actions + (1 - epsilon) * actor_actions\n interpolated_actions.requires_grad_(True)\n \n # Compute Q1 and Q2 for interpolated actions\n q1_interp = self.qf1(obs, interpolated_actions)\n q2_interp = self.qf2(obs, interpolated_actions)\n \n # Compute gradients\n grad1 = torch.autograd.grad(\n outputs=q1_interp,\n inputs=interpolated_actions,\n grad_outputs=torch.ones_like(q1_interp),\n create_graph=True,\n retain_graph=True,\n )[0]\n grad2 = torch.autograd.grad(\n outputs=q2_interp,\n inputs=interpolated_actions,\n grad_outputs=torch.ones_like(q2_interp),\n create_graph=True,\n )[0]\n \n # Gradient norm penalty\n grad_norm1 = grad1.norm(2, dim=1)\n grad_norm2 = grad2.norm(2, dim=1)\n penalty1 = ((grad_norm1 - 1) ** 2).mean()\n penalty2 = ((grad_norm2 - 1) ** 2).mean()\n return (penalty1 + penalty2) * 0.5\n\n def update(self, batch):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n with torch.no_grad():\n # Target policy smoothing\n noise = (torch.randn_like(actions) * self.policy_noise).clamp(\n -self.noise_clip, self.noise_clip\n ) * self.max_action\n next_actions = (self.target_actor(next_obs) + noise).clamp(\n -self.max_action, self.max_action\n )\n target_q1 = self.qf1_target(next_obs, next_actions).view(-1)\n target_q2 = self.qf2_target(next_obs, next_actions).view(-1)\n target_q = torch.min(target_q1, target_q2)\n td_target = rewards + (1 - dones) * self.gamma * target_q\n\n # Current Q estimates\n q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n\n # TD loss\n critic_loss = F.mse_loss(q1, td_target) + F.mse_loss(q2, td_target)\n\n # Gradient penalty\n gp_loss = self.compute_gradient_penalty(obs, actions)\n total_critic_loss = critic_loss + self.gp_lambda * gp_loss\n\n self.q_optimizer.zero_grad()\n total_critic_loss.backward()\n self.q_optimizer.step()\n\n actor_loss_val = 0.0\n if self.total_it % self.policy_frequency == 0:\n # Actor loss: maximize Q1 (or both)\n actor_actions = self.actor(obs)\n q1_pi = self.qf1(obs, actor_actions).view(-1)\n actor_loss = -q1_pi.mean()\n self.actor_optimizer.zero_grad()\n actor_loss.backward()\n self.actor_optimizer.step()\n actor_loss_val = actor_loss.item()\n\n # Soft update target networks\n soft_update(self.target_actor, self.actor, self.tau)\n soft_update(self.qf1_target, self.qf1, self.tau)\n soft_update(self.qf2_target, self.qf2, self.tau)\n\n return {\"critic_loss\": critic_loss.item(), \"gp_loss\": gp_loss.item(), \"actor_loss\": actor_loss_val}"
7}Tool ResultStep 5
OK: Replaced lines 153..299 with 166 line(s) in cleanrl/cleanrl/custom_offpolicy_continuous.py. Editable range: 153–318. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–318 | total: 399 lines] 150| # ===================================================================== 151| # EDITABLE: Network definitions and OffPolicyAlgorithm 152| # ===================================================================== 153 class Actor(nn.Module): 154 """Actor network. Must implement forward(obs) and get_action(obs). 155 156 forward(obs) -> action tensor (used for training). 157 get_action(obs) -> action tensor (used for evaluation, no grad). 158 """ 159 160 def __init__(self, obs_dim, action_dim, max_action): 161 super().__init__() 162 self.max_action = max_action 163 self.fc1 = nn.Linear(obs_dim, 256) 164 self.fc2 = nn.Linear(256, 256) 165 self.fc_m
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[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### halfcheetah-v4 (scripts/halfcheetah.sh) Training metrics (last steps): TRAIN_METRICS step=995000 critic_loss=82.6341 gp_loss=47.0987 actor_loss=-816.7924 TRAIN_METRICS step=996000 critic_loss=67.5595 gp_loss=48.9649 actor_loss=-818.0002 TRAIN_METRICS step=997000 critic_loss=70.2557 gp_loss=51.7806 actor_loss=-830.4521 TRAIN_METRICS step=998000 critic_loss=48.6196 gp_loss=36.9975 actor_loss=-826.9570 TRAIN_METRICS step=999000 critic_loss=81.9966 gp_loss=40.3413 actor_loss=-827.8655 Evaluation (halfcheetah-v4): Eval episodic_return: 10762.53 Eval episodic_return: 10340.22 Eval episodic_return: 10561.24 Final eval return: 10561.24 ### reacher-v4 (scripts/reacher.sh) Training metrics (last steps): TRAIN_METRICS step=995000 critic_loss=0.0504 gp_loss=0.0415 actor_loss=20.0506 TRAIN_METRICS step=996000 critic_loss=0.0654 gp_loss=0.0672 actor_loss=20.2779 TRAIN_METRICS step=997000 critic_loss=0.0579 gp_loss=0.0868 acto
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1{
2 "op": "replace",
3 "filename": "cleanrl/cleanrl/custom_offpolicy_continuous.py",
4 "start_line": 153,
5 "end_line": 318,
6 "content": "class Actor(nn.Module):\n \"\"\"Actor network. Must implement forward(obs) and get_action(obs).\n\n forward(obs) -> action tensor (used for training).\n get_action(obs) -> action tensor (used for evaluation, no grad).\n \"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action):\n super().__init__()\n self.max_action = max_action\n self.fc1 = nn.Linear(obs_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc_mu = nn.Linear(256, action_dim)\n self.register_buffer(\"action_scale\", torch.tensor(max_action, dtype=torch.float32))\n\n def forward(self, obs):\n x = F.relu(self.fc1(obs))\n x = F.relu(self.fc2(x))\n return torch.tanh(self.fc_mu(x)) * self.action_scale\n\n @torch.no_grad()\n def get_action(self, obs):\n return self.forward(obs)\n\n\nclass QNetwork(nn.Module):\n \"\"\"Q-function Q(s, a) -> scalar.\"\"\"\n\n def __init__(self, obs_dim, action_dim):\n super().__init__()\n self.fc1 = nn.Linear(obs_dim + action_dim, 256)\n self.fc2 = nn.Linear(256, 256)\n self.fc3 = nn.Linear(256, 1)\n\n def forward(self, obs, action):\n x = torch.cat([obs, action], dim=-1)\n x = F.relu(self.fc1(x))\n x = F.relu(self.fc2(x))\n return self.fc3(x)\n\n\nclass OffPolicyAlgorithm:\n \"\"\"TD3-GPC: Twin Delayed DDPG with Gradient Penalty, Consensus loss, and adaptive exploration.\"\"\"\n\n def __init__(self, obs_dim, action_dim, max_action, device, args):\n self.device = device\n self.max_action = max_action\n self.gamma = args.gamma\n self.tau = args.tau\n self.exploration_noise = args.exploration_noise\n self.policy_frequency = args.policy_frequency\n self.policy_noise = 0.2\n self.noise_clip = 0.5\n self.total_it = 0\n # Gradient penalty hyperparameter\n self.gp_lambda = 0.1\n # Consensus loss weight\n self.consensus_weight = 0.05\n # Adaptive exploration\n self.beta = 0.1\n self.min_noise_scale = 0.5\n self.max_noise_scale = 2.0\n\n # Actor and target\n self.actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor = Actor(obs_dim, action_dim, max_action).to(device)\n self.target_actor.load_state_dict(self.actor.state_dict())\n\n # Twin critics and targets\n self.qf1 = QNetwork(obs_dim, action_dim).to(device)\n self.qf2 = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf2_target = QNetwork(obs_dim, action_dim).to(device)\n self.qf1_target.load_state_dict(self.qf1.state_dict())\n self.qf2_target.load_state_dict(self.qf2.state_dict())\n\n self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=args.learning_rate)\n self.q_optimizer = optim.Adam(\n list(self.qf1.parameters()) + list(self.qf2.parameters()),\n lr=args.learning_rate,\n )\n\n def select_action(self, obs):\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n with torch.no_grad():\n action = self.actor(obs_t)\n # Compute Q-values for this action (no noise) to estimate uncertainty\n q1 = self.qf1(obs_t, action).item()\n q2 = self.qf2(obs_t, action).item()\n q_std = abs(q1 - q2)\n # Adaptive noise scaling based on Q uncertainty\n noise_scale = self.exploration_noise * (1.0 + self.beta * q_std)\n noise_scale = np.clip(noise_scale, self.min_noise_scale, self.max_noise_scale)\n action = action.cpu().numpy().flatten()\n noise = np.random.normal(0, self.max_action * noise_scale, size=action.shape)\n return np.clip(action + noise, -self.max_action, self.max_action)\n\n def compute_gradient_penalty(self, obs, actions):\n \"\"\"Compute gradient penalty for critic networks (WGAN-GP style).\"\"\"\n # Random interpolation between real actions and policy actions\n actor_actions = self.actor(obs)\n epsilon = torch.rand(obs.size(0), 1, device=self.device)\n interpolated_actions = epsilon * actions + (1 - epsilon) * actor_actions\n interpolated_actions.requires_grad_(True)\n \n # Compute Q1 and Q2 for interpolated actions\n q1_interp = self.qf1(obs, interpolated_actions)\n q2_interp = self.qf2(obs, interpolated_actions)\n \n # Compute gradients\n grad1 = torch.autograd.grad(\n outputs=q1_interp,\n inputs=interpolated_actions,\n grad_outputs=torch.ones_like(q1_interp),\n create_graph=True,\n retain_graph=True,\n )[0]\n grad2 = torch.autograd.grad(\n outputs=q2_interp,\n inputs=interpolated_actions,\n grad_outputs=torch.ones_like(q2_interp),\n create_graph=True,\n )[0]\n \n # Gradient norm penalty\n grad_norm1 = grad1.norm(2, dim=1)\n grad_norm2 = grad2.norm(2, dim=1)\n penalty1 = ((grad_norm1 - 1) ** 2).mean()\n penalty2 = ((grad_norm2 - 1) ** 2).mean()\n return (penalty1 + penalty2) * 0.5\n\n def update(self, batch):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n with torch.no_grad():\n # Target policy smoothing\n noise = (torch.randn_like(actions) * self.policy_noise).clamp(\n -self.noise_clip, self.noise_clip\n ) * self.max_action\n next_actions = (self.target_actor(next_obs) + noise).clamp(\n -self.max_action, self.max_action\n )\n target_q1 = self.qf1_target(next_obs, next_actions).view(-1)\n target_q2 = self.qf2_target(next_obs, next_actions).view(-1)\n target_q = torch.min(target_q1, target_q2)\n td_target = rewards + (1 - dones) * self.gamma * target_q\n\n # Current Q estimates\n q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n\n # TD loss\n critic_loss = F.mse_loss(q1, td_target) + F.mse_loss(q2, td_target)\n \n # Consensus loss (reduce disagreement between Q networks)\n consensus_loss = F.mse_loss(q1, q2) * self.consensus_weight\n\n # Gradient penalty\n gp_loss = self.compute_gradient_penalty(obs, actions)\n total_critic_loss = critic_loss + consensus_loss + self.gp_lambda * gp_loss\n\n self.q_optimizer.zero_grad()\n total_critic_loss.backward()\n self.q_optimizer.step()\n\n actor_loss_val = 0.0\n if self.total_it % self.policy_frequency == 0:\n # Actor loss: maximize Q1 (or both)\n actor_actions = self.actor(obs)\n q1_pi = self.qf1(obs, actor_actions).view(-1)\n actor_loss = -q1_pi.mean()\n self.actor_optimizer.zero_grad()\n actor_loss.backward()\n self.actor_optimizer.step()\n actor_loss_val = actor_loss.item()\n\n # Soft update target networks\n soft_update(self.target_actor, self.actor, self.tau)\n soft_update(self.qf1_target, self.qf1, self.tau)\n soft_update(self.qf2_target, self.qf2, self.tau)\n\n return {\"critic_loss\": critic_loss.item(), \"consensus_loss\": consensus_loss.item(), \"gp_loss\": gp_loss.item(), \"actor_loss\": actor_loss_val}"
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OK: Replaced lines 153..318 with 183 line(s) in cleanrl/cleanrl/custom_offpolicy_continuous.py. Editable range: 153–335. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–335 | total: 416 lines] 150| # ===================================================================== 151| # EDITABLE: Network definitions and OffPolicyAlgorithm 152| # ===================================================================== 153 class Actor(nn.Module): 154 """Actor network. Must implement forward(obs) and get_action(obs). 155 156 forward(obs) -> action tensor (used for training). 157 get_action(obs) -> action tensor (used for evaluation, no grad). 158 """ 159 160 def __init__(self, obs_dim, action_dim, max_action): 161 super().__init__() 162 self.max_action = max_action 163 self.fc1 = nn.Linear(obs_dim, 256) 164 self.fc2 = nn.Linear(256, 256) 165 self.fc_m
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[Test #3] (0 tests remaining) ## Seed 42 ### halfcheetah-v4 (scripts/halfcheetah.sh) Training metrics (last steps): TRAIN_METRICS step=45000 critic_loss=3.3173 consensus_loss=0.0303 gp_loss=0.5027 actor_loss=-15.9994 TRAIN_METRICS step=46000 critic_loss=3.0428 consensus_loss=0.0286 gp_loss=0.6743 actor_loss=-15.3041 TRAIN_METRICS step=47000 critic_loss=3.0170 consensus_loss=0.0288 gp_loss=0.6787 actor_loss=-17.8523 TRAIN_METRICS step=48000 critic_loss=4.4969 consensus_loss=0.0390 gp_loss=0.7803 actor_loss=-20.9203 TRAIN_METRICS step=49000 critic_loss=4.2618 consensus_loss=0.0402 gp_loss=0.7484 actor_loss=-20.4717 Evaluation (halfcheetah-v4): Eval episodic_return: -1.71 Eval episodic_return: -455.42 Eval episodic_return: 226.60 Final eval return: 226.60 ### reacher-v4 (scripts/reacher.sh) Training metrics (last steps): TRAIN_METRICS step=45000 critic_loss=0.0971 consensus_loss=0.0017 gp_loss=0.2729 actor_loss=8.7878 TRAIN_METRICS step=46000 critic_loss=0.0946 consensus_loss=0.0020 g