Agent Conversation: google/gemini-3.1-pro-preview
Model: google/gemini-3.1-pro-preview
Total steps: 6
Messages: 13
UserStep 0
Task: rl-value-discrete
Online RL: Value-Based Methods for Discrete Control
Objective
Design and implement a value-based RL algorithm for discrete action spaces. Your code goes in custom_value_discrete.py. Three reference implementations (DQN, DoubleDQN, C51) are provided as read-only.
Background
Value-based methods estimate Q-values Q(s,a) for each state-action pair and derive a policy by selecting actions with the highest Q-value. Key challenges include overestimation bias, sample efficiency, and representing uncertainty. Different approaches address these through double Q-learning, distributional value functions, or prioritized replay.
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 CartPole-v1, LunarLander-v2, Acrobot-v1. 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_value_discrete.py [EDITABLE — lines 174–242 only]
1: # Custom value-based discrete RL algorithm for MLS-Bench
2: #
3: # EDITABLE section: QNetwork head and ValueAlgorithm classes.
4: # FIXED sections: everything else (config, env, buffer, encoder, utility, 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 = "CartPole-v1"
35: """the id of the environment"""
36: total_timesteps: int = 500000
37: """total timesteps of the experiments"""
38: learning_rate: float = 2.5e-4
39: """the learning rate of the optimizer"""
40: buffer_size: int = 10000
41: """the replay memory buffer size"""
42: gamma: float = 0.99
43: """the discount factor gamma"""AssistantStep 1edit
Tool Input
1{
2 "filename": "cleanrl/cleanrl/custom_value_discrete.py",
3 "end_line": 242,
4 "start_line": 174,
5 "content": "class QNetwork(nn.Module):\n \"\"\"M-DQN with Dueling Architecture\"\"\"\n def __init__(self, obs_dim, n_actions):\n super().__init__()\n self.encoder = MLPEncoder(obs_dim)\n self.value_head = nn.Linear(ENCODER_FEATURE_DIM, 1)\n self.advantage_head = nn.Linear(ENCODER_FEATURE_DIM, n_actions)\n\n def forward(self, obs):\n features = self.encoder(obs)\n val = self.value_head(features)\n adv = self.advantage_head(features)\n return val + adv - adv.mean(dim=1, keepdim=True)\n\n\nclass ValueAlgorithm:\n def __init__(self, obs_dim, n_actions, device, args):\n self.device = device\n self.n_actions = n_actions\n self.gamma = args.gamma\n self.total_it = 0\n\n self.tau = 0.03\n self.alpha = 0.9\n\n self.q_network = QNetwork(obs_dim, n_actions).to(device)\n self.target_network = QNetwork(obs_dim, n_actions).to(device)\n self.target_network.load_state_dict(self.q_network.state_dict())\n\n self.optimizer = optim.Adam(self.q_network.parameters(), lr=args.learning_rate)\n\n def select_action(self, obs, epsilon):\n if random.random() < epsilon:\n return random.randint(0, self.n_actions - 1)\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n q_values = self.q_network(obs_t)\n return torch.argmax(q_values, dim=1).item()\n\n def update(self, batch, global_step):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n with torch.no_grad():\n q_next_target = self.target_network(next_obs)\n pi_next = F.softmax(q_next_target / self.tau, dim=1)\n log_pi_next = F.log_softmax(q_next_target / self.tau, dim=1)\n v_next = (pi_next * (q_next_target - self.tau * log_pi_next)).sum(dim=1)\n\n q_online_main = self.q_network(obs)\n log_pi_online = F.log_softmax(q_online_main / self.tau, dim=1)\n log_pi_a = log_pi_online.gather(1, actions.unsqueeze(1)).squeeze(1)\n munchausen_term = self.alpha * self.tau * torch.clamp(log_pi_a, min=-1.0, max=0.0)\n\n td_target = rewards + munchausen_term + self.gamma * (1 - dones) * v_next\n\n q_online = self.q_network(obs)\n q_a = q_online.gather(1, actions.unsqueeze(1)).squeeze(1)\n\n td_loss = F.smooth_l1_loss(q_a, td_target)\n\n self.optimizer.zero_grad()\n td_loss.backward()\n torch.nn.utils.clip_grad_norm_(self.q_network.parameters(), max_norm=10.0)\n self.optimizer.step()\n\n return {\"td_loss\": td_loss.item(), \"q_values\": q_a.mean().item()}",
6 "op": "replace"
7}Tool ResultStep 1
OK: Replaced lines 174..242 with 66 line(s) in cleanrl/cleanrl/custom_value_discrete.py. Editable range: 174–239. [Current file: cleanrl/cleanrl/custom_value_discrete.py | editable: 174–239 | total: 326 lines] 171| # ===================================================================== 172| # EDITABLE: QNetwork head and ValueAlgorithm 173| # ===================================================================== 174 class QNetwork(nn.Module): 175 """M-DQN with Dueling Architecture""" 176 def __init__(self, obs_dim, n_actions): 177 super().__init__() 178 self.encoder = MLPEncoder(obs_dim) 179 self.value_head = nn.Linear(ENCODER_FEATURE_DIM, 1) 180 self.advantage_head = nn.Linear(ENCODER_FEATURE_DIM, n_actions) 181 182 def forward(self, obs): 183 features = self.encoder(obs) 184 val = self.value_head(features) 185 adv = self.advantage_head(features) 186
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result) ## Seed 42 ### cartpole-v1 (scripts/cartpole.sh) Training metrics (last steps): TRAIN_METRICS step=495000 td_loss=19144922.0000 q_values=48618360.0000 TRAIN_METRICS step=496000 td_loss=15134479.0000 q_values=47388760.0000 TRAIN_METRICS step=497000 td_loss=14842780.0000 q_values=53110176.0000 TRAIN_METRICS step=498000 td_loss=18958728.0000 q_values=51224464.0000 TRAIN_METRICS step=499000 td_loss=15353181.0000 q_values=47306536.0000 Evaluation (cartpole-v1): Eval episodic_return: 9.10 Eval episodic_return: 9.10 Eval episodic_return: 9.10 Final eval return: 9.10 ### lunarlander-v2 (scripts/lunarlander.sh) Training metrics (last steps): TRAIN_METRICS step=495000 td_loss=0.4940 q_values=9.9950 TRAIN_METRICS step=496000 td_loss=0.4101 q_values=12.1695 TRAIN_METRICS step=497000 td_loss=0.1288 q_values=11.7109 TRAIN_METRICS step=498000 td_loss=0.1434 q_values=13.5375 TRAIN_METRICS step=499000 td_loss=0.1208 q_values=10.4
AssistantStep 3edit
Tool Input
1{
2 "start_line": 174,
3 "filename": "cleanrl/cleanrl/custom_value_discrete.py",
4 "end_line": 242,
5 "op": "replace",
6 "content": "class QNetwork(nn.Module):\n \"\"\"M-DQN with Dueling Architecture\"\"\"\n def __init__(self, obs_dim, n_actions):\n super().__init__()\n self.encoder = MLPEncoder(obs_dim)\n self.value_head = nn.Linear(ENCODER_FEATURE_DIM, 1)\n self.advantage_head = nn.Linear(ENCODER_FEATURE_DIM, n_actions)\n\n def forward(self, obs):\n features = self.encoder(obs)\n val = self.value_head(features)\n adv = self.advantage_head(features)\n return val + adv - adv.mean(dim=1, keepdim=True)\n\n\nclass ValueAlgorithm:\n def __init__(self, obs_dim, n_actions, device, args):\n self.device = device\n self.n_actions = n_actions\n self.gamma = args.gamma\n self.total_it = 0\n\n self.tau = 0.03\n self.alpha = 0.9\n\n self.q_network = QNetwork(obs_dim, n_actions).to(device)\n self.target_network = QNetwork(obs_dim, n_actions).to(device)\n self.target_network.load_state_dict(self.q_network.state_dict())\n\n self.optimizer = optim.Adam(self.q_network.parameters(), lr=args.learning_rate)\n\n def select_action(self, obs, epsilon):\n if random.random() < epsilon:\n return random.randint(0, self.n_actions - 1)\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n q_values = self.q_network(obs_t)\n return torch.argmax(q_values, dim=1).item()\n\n def update(self, batch, global_step):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n with torch.no_grad():\n q_next_target = self.target_network(next_obs)\n pi_next = F.softmax(q_next_target / self.tau, dim=1)\n log_pi_next = F.log_softmax(q_next_target / self.tau, dim=1)\n v_next = (pi_next * (q_next_target - self.tau * log_pi_next)).sum(dim=1)\n\n q_online_main = self.q_network(obs)\n log_pi_online = F.log_softmax(q_online_main / self.tau, dim=1)\n log_pi_a = log_pi_online.gather(1, actions.unsqueeze(1)).squeeze(1)\n munchausen_term = self.alpha * self.tau * torch.clamp(log_pi_a, min=-1.0, max=0.0)\n\n td_target = rewards + munchausen_term + self.gamma * (1 - dones) * v_next\n\n q_online = self.q_network(obs)\n q_a = q_online.gather(1, actions.unsqueeze(1)).squeeze(1)\n\n td_loss = F.mse_loss(q_a, td_target)\n\n self.optimizer.zero_grad()\n td_loss.backward()\n torch.nn.utils.clip_grad_norm_(self.q_network.parameters(), max_norm=10.0)\n self.optimizer.step()\n\n return {\"td_loss\": td_loss.item(), \"q_values\": q_a.mean().item()}"
7}Tool ResultStep 3
ERROR: Lines 174..242 exceed the editable range. You may only edit lines 174–239. [Current file: cleanrl/cleanrl/custom_value_discrete.py | editable: 174–239 | total: 326 lines] 171| # ===================================================================== 172| # EDITABLE: QNetwork head and ValueAlgorithm 173| # ===================================================================== 174 class QNetwork(nn.Module): 175 """M-DQN with Dueling Architecture""" 176 def __init__(self, obs_dim, n_actions): 177 super().__init__() 178 self.encoder = MLPEncoder(obs_dim) 179 self.value_head = nn.Linear(ENCODER_FEATURE_DIM, 1) 180 self.advantage_head = nn.Linear(ENCODER_FEATURE_DIM, n_actions) 181 182 def forward(self, obs): 183 features = self.encoder(obs) 184 val = self.value_head(features) 185 adv = self.advantage_head(features) 186 return val + adv - adv.mean(dim
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result) ### cartpole-v1 (scripts/cartpole.sh) Training metrics (last steps): TRAIN_METRICS step=495000 td_loss=19144922.0000 q_values=48618360.0000 TRAIN_METRICS step=496000 td_loss=15134479.0000 q_values=47388760.0000 TRAIN_METRICS step=497000 td_loss=14842780.0000 q_values=53110176.0000 TRAIN_METRICS step=498000 td_loss=18958728.0000 q_values=51224464.0000 TRAIN_METRICS step=499000 td_loss=15353181.0000 q_values=47306536.0000 Evaluation (cartpole-v1): Eval episodic_return: 9.10 Eval episodic_return: 9.10 Eval episodic_return: 9.10 Final eval return: 9.10 ### lunarlander-v2 (scripts/lunarlander.sh) Training metrics (last steps): TRAIN_METRICS step=495000 td_loss=0.4940 q_values=9.9950 TRAIN_METRICS step=496000 td_loss=0.4101 q_values=12.1695 TRAIN_METRICS step=497000 td_loss=0.1288 q_values=11.7109 TRAIN_METRICS step=498000 td_loss=0.1434 q_values=13.5375 TRAIN_METRICS step=499000 td_loss=0.1208 q_values=10.4010 Evaluation
AssistantStep 5edit
Tool Input
1{
2 "filename": "cleanrl/cleanrl/custom_value_discrete.py",
3 "end_line": 242,
4 "start_line": 174,
5 "content": "class QNetwork(nn.Module):\n \"\"\"Double Dueling Distributional Q-Network\"\"\"\n def __init__(self, obs_dim, n_actions, n_quantiles=50):\n super().__init__()\n self.n_actions = n_actions\n self.n_quantiles = n_quantiles\n self.encoder = MLPEncoder(obs_dim)\n \n self.value_head = nn.Linear(ENCODER_FEATURE_DIM, n_quantiles)\n self.advantage_head = nn.Linear(ENCODER_FEATURE_DIM, n_actions * n_quantiles)\n\n def forward(self, obs):\n quantiles = self.get_quantiles(obs)\n return quantiles.mean(dim=2)\n\n def get_quantiles(self, obs):\n features = self.encoder(obs)\n v = self.value_head(features).view(-1, 1, self.n_quantiles)\n a = self.advantage_head(features).view(-1, self.n_actions, self.n_quantiles)\n return v + a - a.mean(dim=1, keepdim=True)\n\n\nclass ValueAlgorithm:\n \"\"\"Algorithm: Distributional Double Dueling DQN (D3QR-DQN)\"\"\"\n def __init__(self, obs_dim, n_actions, device, args):\n self.device = device\n self.n_actions = n_actions\n self.gamma = args.gamma\n self.n_quantiles = 50\n self.total_it = 0\n\n self.q_network = QNetwork(obs_dim, n_actions, self.n_quantiles).to(device)\n self.target_network = QNetwork(obs_dim, n_actions, self.n_quantiles).to(device)\n self.target_network.load_state_dict(self.q_network.state_dict())\n\n self.optimizer = optim.Adam(self.q_network.parameters(), lr=args.learning_rate)\n\n self.tau = torch.arange(1, self.n_quantiles + 1, dtype=torch.float32, device=device)\n self.tau = (2 * self.tau - 1) / (2 * self.n_quantiles)\n\n def select_action(self, obs, epsilon):\n if random.random() < epsilon:\n return random.randint(0, self.n_actions - 1)\n obs_t = torch.tensor(obs.reshape(1, -1), device=self.device, dtype=torch.float32)\n q_values = self.q_network(obs_t)\n return torch.argmax(q_values, dim=1).item()\n\n def update(self, batch, global_step):\n self.total_it += 1\n obs, next_obs, actions, rewards, dones = batch\n\n with torch.no_grad():\n next_quantiles_online = self.q_network.get_quantiles(next_obs)\n next_actions = next_quantiles_online.mean(dim=2).argmax(dim=1)\n \n next_quantiles = self.target_network.get_quantiles(next_obs)\n next_quantiles_best = next_quantiles[torch.arange(len(next_obs)), next_actions]\n \n target_quantiles = rewards.unsqueeze(1) + self.gamma * next_quantiles_best * (1 - dones.unsqueeze(1))\n\n current_quantiles = self.q_network.get_quantiles(obs)\n current_quantiles = current_quantiles[torch.arange(len(obs)), actions]\n\n curr_q = current_quantiles.unsqueeze(2) # [batch, n_quantiles, 1]\n targ_q = target_quantiles.unsqueeze(1) # [batch, 1, n_quantiles]\n diff = targ_q - curr_q # [batch, n_quantiles, n_quantiles]\n\n hub_loss = F.huber_loss(curr_q, targ_q, reduction='none', delta=1.0)\n \n tau_view = self.tau.view(1, -1, 1)\n weight = torch.abs(tau_view - (diff.detach() < 0).float())\n \n quantile_loss = (weight * hub_loss).mean(dim=2).sum(dim=1).mean()\n\n self.optimizer.zero_grad()\n quantile_loss.backward()\n torch.nn.utils.clip_grad_norm_(self.q_network.parameters(), max_norm=10.0)\n self.optimizer.step()\n\n return {\"td_loss\": quantile_loss.item(), \"q_values\": current_quantiles.mean().item()}",
6 "op": "replace"
7}Tool ResultStep 5
ERROR: Lines 174..242 exceed the editable range. You may only edit lines 174–239. [Current file: cleanrl/cleanrl/custom_value_discrete.py | editable: 174–239 | total: 326 lines] 171| # ===================================================================== 172| # EDITABLE: QNetwork head and ValueAlgorithm 173| # ===================================================================== 174 class QNetwork(nn.Module): 175 """M-DQN with Dueling Architecture""" 176 def __init__(self, obs_dim, n_actions): 177 super().__init__() 178 self.encoder = MLPEncoder(obs_dim) 179 self.value_head = nn.Linear(ENCODER_FEATURE_DIM, 1) 180 self.advantage_head = nn.Linear(ENCODER_FEATURE_DIM, n_actions) 181 182 def forward(self, obs): 183 features = self.encoder(obs) 184 val = self.value_head(features) 185 adv = self.advantage_head(features) 186 return val + adv - adv.mean(dim
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining) ## Seed 42 ### cartpole-v1 (scripts/cartpole.sh) Training metrics (last steps): TRAIN_METRICS step=495000 td_loss=19144922.0000 q_values=48618360.0000 TRAIN_METRICS step=496000 td_loss=15134479.0000 q_values=47388760.0000 TRAIN_METRICS step=497000 td_loss=14842780.0000 q_values=53110176.0000 TRAIN_METRICS step=498000 td_loss=18958728.0000 q_values=51224464.0000 TRAIN_METRICS step=499000 td_loss=15353181.0000 q_values=47306536.0000 Evaluation (cartpole-v1): Eval episodic_return: 9.10 Eval episodic_return: 9.10 Eval episodic_return: 9.10 Final eval return: 9.10 ### lunarlander-v2 (scripts/lunarlander.sh) Training metrics (last steps): TRAIN_METRICS step=495000 td_loss=0.4940 q_values=9.9950 TRAIN_METRICS step=496000 td_loss=0.4101 q_values=12.1695 TRAIN_METRICS step=497000 td_loss=0.1288 q_values=11.7109 TRAIN_METRICS step=498000 td_loss=0.1434 q_values=13.5375 TRAIN_METRICS step=499000 td_loss=0.1208 q_values=10.4010 Evaluation (lunarlander-v2): Eval ep