Agent Conversation: google/gemini-3.1-pro-preview
Model: google/gemini-3.1-pro-preview
Total steps: 6
Messages: 13
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 "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 \"\"\"Soft Pessimistic Q-Learning (SPQ).\"\"\"\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.softmin_alpha = 2.0\n self.total_it = 0\n\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 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 _unbiased_softmin(self, x, y):\n stacked = torch.stack([-self.softmin_alpha * x, -self.softmin_alpha * y], dim=0)\n return - (1.0 / self.softmin_alpha) * (torch.logsumexp(stacked, dim=0) - np.log(2.0))\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 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 \n soft_q = self._unbiased_softmin(target_q1, target_q2)\n td_target = rewards + (1 - dones) * self.gamma * soft_q\n\n q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n critic_loss = F.smooth_l1_loss(q1, td_target) + F.smooth_l1_loss(q2, td_target)\n\n self.q_optimizer.zero_grad()\n 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 pi = self.actor(obs)\n q1_pi = self.qf1(obs, pi).view(-1)\n q2_pi = self.qf2(obs, pi).view(-1)\n \n actor_obj = self._unbiased_softmin(q1_pi, q2_pi)\n actor_loss = -actor_obj.mean()\n\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(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(), \"actor_loss\": actor_loss_val}\n",
5 "start_line": 153,
6 "end_line": 246
7}Tool ResultStep 1
ERROR: Lines 153..246 exceed the editable range. You may only edit lines 153–244. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–244 | total: 325 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_mu = nn.Linear(256, action_dim) 166
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call test(submit=1) to submit this result) ## Seed 42 ### halfcheetah-v4 (scripts/halfcheetah.sh) Evaluation (halfcheetah-v4): Eval episodic_return: -1.71 Eval episodic_return: -1.71 Final eval return: -1.71 ### reacher-v4 (scripts/reacher.sh) Evaluation (reacher-v4): Eval episodic_return: -40.86 Eval episodic_return: -40.86 Final eval return: -40.86 ## Seed 123 ### halfcheetah-v4 (scripts/halfcheetah.sh) Evaluation (halfcheetah-v4): Eval episodic_return: -0.31 Eval episodic_return: -0.31 Final eval return: -0.31 ### reacher-v4 (scripts/reacher.sh) Evaluation (reacher-v4): Eval episodic_return: -21.79 Eval episodic_return: -21.79 Final eval return: -21.79 ## Seed 456 ### halfcheetah-v4 (scripts/halfcheetah.sh) Evaluation (halfcheetah-v4): Eval episodic_return: -2.73 Eval episodic_return: -2.73 Final eval return: -2.73 ### reacher-v4 (scripts/reacher.sh) Evaluation (reacher-v4): Eval episodic_return: -36.71 Eval episodic_return: -36.71 Final eva
AssistantStep 3edit
Tool Input
1{
2 "content": " q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n critic_loss = F.mse_loss(q1, td_target) + F.mse_loss(q2, td_target)\n\n self.q_optimizer.zero_grad()\n critic_loss.backward()",
3 "start_line": 223,
4 "end_line": 227,
5 "filename": "cleanrl/cleanrl/custom_offpolicy_continuous.py",
6 "op": "replace"
7}Tool ResultStep 3
OK: Replaced lines 223..227 with 6 line(s) in cleanrl/cleanrl/custom_offpolicy_continuous.py. Editable range: 153–245. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–245 | total: 326 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_mu
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call test(submit=2) to submit this result)
### halfcheetah-v4 (scripts/halfcheetah.sh)
/usr/bin/bash: warning: setlocale: LC_ALL: cannot change locale (zh_CN.UTF-8)
Gym has been unmaintained since 2022 and does not support NumPy 2.0 amongst other critical functionality.
Please upgrade to Gymnasium, the maintained drop-in replacement of Gym, or contact the authors of your software and request that they upgrade.
See the migration guide at https://gymnasium.farama.org/introduction/migration_guide/ for additional information.
/usr/local/lib/python3.8/dist-packages/pygame/pkgdata.py:25: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
from pkg_resources import resource_stream, resource_exists
Traceback (most recent call last):
File "cleanrl/custom_offpolicy_continuous.py", line 272, in <module>
algorithm = OffPolicyAlgorithm(obs_dim, action_dim, max_action, device, args)
File "clean
AssistantStep 5edit
Tool Input
1{
2 "filename": "cleanrl/cleanrl/custom_offpolicy_continuous.py",
3 "end_line": 244,
4 "op": "replace",
5 "start_line": 153,
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 \"\"\"Weighted Delayed DDPG (WD3).\n \n Implements Weighted Double Q-learning to mitigate severe underestimation bias \n present in standard TD3, and uses a Symmetric Mean-Q actor objective for stable gradients.\n \"\"\"\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.target_w = 0.75 # Weighted target parameter\n self.total_it = 0\n\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 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 with torch.no_grad():\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 \n min_q = torch.min(target_q1, target_q2)\n max_q = torch.max(target_q1, target_q2)\n # Weighted average mitigates TD3 strict underestimation\n target_q = self.target_w * min_q + (1.0 - self.target_w) * max_q\n td_target = rewards + (1 - dones) * self.gamma * target_q\n\n q1 = self.qf1(obs, actions).view(-1)\n q2 = self.qf2(obs, actions).view(-1)\n critic_loss = F.mse_loss(q1, td_target) + F.mse_loss(q2, td_target)\n\n self.q_optimizer.zero_grad()\n 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 pi = self.actor(obs)\n # Symmetric update improves stability\n q1_pi = self.qf1(obs, pi).view(-1)\n q2_pi = self.qf2(obs, pi).view(-1)\n actor_loss = -((q1_pi + q2_pi) / 2.0).mean()\n\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(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(), \"actor_loss\": actor_loss_val}\n"
7}Tool ResultStep 5
OK: Replaced lines 153..244 with 130 line(s) in cleanrl/cleanrl/custom_offpolicy_continuous.py. Editable range: 153–283. [Current file: cleanrl/cleanrl/custom_offpolicy_continuous.py | editable: 153–283 | total: 364 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
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining; auto-submitted as final) ## Seed 42 ### halfcheetah-v4 (scripts/halfcheetah.sh) Training metrics (last steps): TRAIN_METRICS step=943000 critic_loss=34.8014 actor_loss=-921.1854 TRAIN_METRICS step=944000 critic_loss=39.6355 actor_loss=-918.9861 TRAIN_METRICS step=945000 critic_loss=36.0014 actor_loss=-914.0410 TRAIN_METRICS step=946000 critic_loss=56.2638 actor_loss=-904.4093 TRAIN_METRICS step=947000 critic_loss=36.8706 actor_loss=-916.1273 Evaluation (halfcheetah-v4): Eval episodic_return: 11310.83 Eval episodic_return: 11028.44 Eval episodic_return: 11352.69 Final eval return: 11352.69 ### reacher-v4 (scripts/reacher.sh) Training metrics (last steps): TRAIN_METRICS step=995000 critic_loss=0.0192 actor_loss=11.6957 TRAIN_METRICS step=996000 critic_loss=0.0214 actor_loss=11.8788 TRAIN_METRICS step=997000 critic_loss=0.0218 actor_loss=11.8147 TRAIN_METRICS step=998000 critic_loss=0.0203 actor_loss=11.7913 TRAIN_METRICS step=999000 critic_loss=0.0198 ac