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Model: gemini-3.1-pro-preview

Total steps: 8

Messages: 18

UserStep 0

Task: rl-reward-learning

Inverse RL: Reward Learning from Expert Demonstrations

Objective

Design and implement an inverse reinforcement learning (IRL) algorithm that learns a reward function from expert demonstrations. Your code goes in custom_irl.py, specifically the RewardNetwork and IRLAlgorithm classes. Three reference implementations (GAIL, AIRL, BC) from the imitation library are provided as read-only context.

Background

Inverse reinforcement learning recovers a reward function that explains observed expert behavior. The learned reward is then used to train a policy via standard RL (PPO in this benchmark). Key challenges include:

Designing reward network architectures that capture the structure of expert behavior
Balancing discriminator training with policy improvement
Avoiding reward hacking where the policy exploits learned reward artifacts
Ensuring the learned reward generalizes across different states visited during training

Different IRL approaches address these through adversarial training (GAIL), potential-based reward shaping (AIRL), or direct behavioral cloning. Your goal is to design a novel reward network architecture or IRL training algorithm that outperforms these baselines.

Evaluation

Trained and evaluated on three MuJoCo locomotion environments using pre-generated expert demonstrations: HalfCheetah-v4, Hopper-v4, Walker2d-v4. Metric: mean episodic return over 10 evaluation episodes (higher is better). The policy is trained using PPO with the learned reward signal.

imitation/custom_irl.py [EDITABLE — lines 231–357 only]

     1: # Custom IRL / Reward Learning algorithm for MLS-Bench
     2: #
     3: # EDITABLE section: RewardNetwork and IRLAlgorithm classes.
     4: # FIXED sections: everything else (config, env, demo loading, PPO training, evaluation).
     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: 
    17: 
    18: # =====================================================================
    19: # FIXED: Configuration
    20: # =====================================================================
    21: @dataclass
    22: class Args:
    23:     env_id: str = "HalfCheetah-v4"
    24:     seed: int = 42
    25:     torch_deterministic: bool = True
    26:     cuda: bool = True
    27:     # IRL training
    28:     irl_epochs: int = 200
    29:     irl_batch_size: int = 256
    30:     irl_lr: float = 3e-4
    31:     demo_path: str = ""  # set from env or CLI
    32:     # Policy training (PPO via custom loop)
    33:     total_timesteps: int = 1000000
    34:     policy_lr: float = 3e-4
    35:     gamma: float = 0.99
    36:     gae_lambda: float = 0.95
    37:     n_steps: int = 2048
    38:     n_epochs: int = 10
    39

AssistantStep 1edit

Tool Input

1{
2  "end_line": 357,
3  "start_line": 231,
4  "filename": "custom_irl.py",
5  "content": "class RewardNetwork(nn.Module):\n    \"\"\"Multi-Task Reward Network with shared state features.\"\"\"\n\n    def __init__(self, obs_dim, action_dim):\n        super().__init__()\n        self.state_net = nn.Sequential(\n            nn.Linear(obs_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n        )\n        self.disc_head = nn.Sequential(\n            nn.Linear(256 + action_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n        )\n        self.bc_head = nn.Linear(256, action_dim)\n        self.bc_coef = 2.0\n\n    def forward(self, state, action, next_state):\n        h = self.state_net(state)\n        h_a = torch.cat([h, action], dim=-1)\n        d_val = self.disc_head(h_a).squeeze(-1)\n        \n        bc_act = self.bc_head(h)\n        penalty = torch.mean((action - bc_act)**2, dim=-1)\n        \n        return d_val - self.bc_coef * penalty\n\n    def compute_components(self, state, action):\n        h = self.state_net(state)\n        h_a = torch.cat([h, action], dim=-1)\n        d_val = self.disc_head(h_a).squeeze(-1)\n        bc_act = self.bc_head(h)\n        return d_val, bc_act\n\n\nclass IRLAlgorithm:\n    \"\"\"LSGAN + BC Regularization Multi-Task IRL.\"\"\"\n\n    def __init__(self, reward_net, expert_demos, obs_dim, action_dim, device, args):\n        self.reward_net = reward_net\n        self.expert_demos = expert_demos\n        self.device = device\n        self.args = args\n        self.obs_dim = obs_dim\n        self.action_dim = action_dim\n\n        self.optimizer = optim.Adam(self.reward_net.parameters(), lr=args.irl_lr)\n        self.total_updates = 0\n        self.grad_penalty_coef = 10.0\n        \n        self._policy = None\n        self._policy_optimizer = None\n\n    def set_policy(self, policy, optimizer):\n        self._policy = policy\n        self._policy_optimizer = optimizer\n\n    def compute_reward(self, obs, acts, next_obs):\n        with torch.no_grad():\n            return self.reward_net(obs, acts, next_obs)\n\n    def update(self, policy_obs, policy_acts, policy_next_obs, policy_dones):\n        self.total_updates += 1\n        batch_size = self.args.irl_batch_size\n\n        n_expert = len(self.expert_demos[\"obs\"])\n        expert_idx = torch.randint(0, n_expert, (batch_size,))\n        expert_obs = self.expert_demos[\"obs\"][expert_idx]\n        expert_acts = self.expert_demos[\"acts\"][expert_idx]\n\n        n_policy = len(policy_obs)\n        policy_idx = torch.randint(0, n_policy, (batch_size,))\n        gen_obs = policy_obs[policy_idx]\n        gen_acts = policy_acts[policy_idx]\n\n        expert_d, expert_bc = self.reward_net.compute_components(expert_obs, expert_acts)\n        gen_d, gen_bc = self.reward_net.compute_components(gen_obs, gen_acts)\n\n        disc_loss = 0.5 * torch.mean((expert_d - 1.0)**2) + 0.5 * torch.mean((gen_d - (-1.0))**2)\n        bc_loss = F.mse_loss(expert_bc, expert_acts)\n\n        alpha = torch.rand(batch_size, 1, device=self.device)\n        interp_obs = (alpha * expert_obs + (1 - alpha) * gen_obs).requires_grad_(True)\n        interp_acts = (alpha * expert_acts + (1 - alpha) * gen_acts).requires_grad_(True)\n        \n        h_interp = self.reward_net.state_net(interp_obs)\n        d_interp = self.reward_net.disc_head(torch.cat([h_interp, interp_acts], dim=-1)).squeeze(-1)\n        \n        ones = torch.ones_like(d_interp)\n        grads = torch.autograd.grad(\n            outputs=d_interp, inputs=[interp_obs, interp_acts],\n            grad_outputs=ones, create_graph=True, retain_graph=True,\n        )\n        grad_cat = torch.cat([g.reshape(batch_size, -1) for g in grads], dim=1)\n        grad_norm = torch.sqrt(torch.sum(grad_cat ** 2, dim=1) + 1e-12)\n        gp = torch.mean((grad_norm - 1.0) ** 2)\n\n        loss = disc_loss + self.grad_penalty_coef * gp + 10.0 * bc_loss\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\n        p_bc_loss_val = 0.0\n        if self._policy is not None:\n            n_bc_steps = 10\n            for _ in range(n_bc_steps):\n                idx = torch.randint(0, n_expert, (batch_size,))\n                exp_o = self.expert_demos[\"obs\"][idx]\n                exp_a = self.expert_demos[\"acts\"][idx]\n                \n                _, log_prob, entropy, _ = self._policy.get_action_and_value(exp_o, exp_a)\n                p_bc_loss = -log_prob.mean() - 0.001 * entropy.mean()\n                \n                self._policy_optimizer.zero_grad()\n                p_bc_loss.backward()\n                nn.utils.clip_grad_norm_(self._policy.parameters(), 0.5)\n                self._policy_optimizer.step()\n                p_bc_loss_val += p_bc_loss.item()\n            p_bc_loss_val /= n_bc_steps\n\n        return {\n            \"irl_loss\": loss.item(),\n            \"disc_loss\": disc_loss.item(),\n            \"bc_loss\": bc_loss.item(),\n            \"p_bc_loss\": p_bc_loss_val\n        }",
6  "op": "replace"
7}

Tool ResultStep 1

ERROR: Package 'custom_irl.py' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "start_line": 231,
3  "op": "replace",
4  "end_line": 357,
5  "filename": "imitation/custom_irl.py",
6  "content": "class RewardNetwork(nn.Module):\n    \"\"\"Multi-Task Reward Network with shared state features.\"\"\"\n\n    def __init__(self, obs_dim, action_dim):\n        super().__init__()\n        self.state_net = nn.Sequential(\n            nn.Linear(obs_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n        )\n        self.disc_head = nn.Sequential(\n            nn.Linear(256 + action_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n        )\n        self.bc_head = nn.Linear(256, action_dim)\n        self.bc_coef = 2.0\n\n    def forward(self, state, action, next_state):\n        h = self.state_net(state)\n        h_a = torch.cat([h, action], dim=-1)\n        d_val = self.disc_head(h_a).squeeze(-1)\n        \n        bc_act = self.bc_head(h)\n        penalty = torch.mean((action - bc_act)**2, dim=-1)\n        \n        return d_val - self.bc_coef * penalty\n\n    def compute_components(self, state, action):\n        h = self.state_net(state)\n        h_a = torch.cat([h, action], dim=-1)\n        d_val = self.disc_head(h_a).squeeze(-1)\n        bc_act = self.bc_head(h)\n        return d_val, bc_act\n\n\nclass IRLAlgorithm:\n    \"\"\"LSGAN + BC Regularization Multi-Task IRL.\"\"\"\n\n    def __init__(self, reward_net, expert_demos, obs_dim, action_dim, device, args):\n        self.reward_net = reward_net\n        self.expert_demos = expert_demos\n        self.device = device\n        self.args = args\n        self.obs_dim = obs_dim\n        self.action_dim = action_dim\n\n        self.optimizer = optim.Adam(self.reward_net.parameters(), lr=args.irl_lr)\n        self.total_updates = 0\n        self.grad_penalty_coef = 10.0\n        \n        self._policy = None\n        self._policy_optimizer = None\n\n    def set_policy(self, policy, optimizer):\n        self._policy = policy\n        self._policy_optimizer = optimizer\n\n    def compute_reward(self, obs, acts, next_obs):\n        with torch.no_grad():\n            return self.reward_net(obs, acts, next_obs)\n\n    def update(self, policy_obs, policy_acts, policy_next_obs, policy_dones):\n        self.total_updates += 1\n        batch_size = self.args.irl_batch_size\n\n        n_expert = len(self.expert_demos[\"obs\"])\n        expert_idx = torch.randint(0, n_expert, (batch_size,))\n        expert_obs = self.expert_demos[\"obs\"][expert_idx]\n        expert_acts = self.expert_demos[\"acts\"][expert_idx]\n\n        n_policy = len(policy_obs)\n        policy_idx = torch.randint(0, n_policy, (batch_size,))\n        gen_obs = policy_obs[policy_idx]\n        gen_acts = policy_acts[policy_idx]\n\n        expert_d, expert_bc = self.reward_net.compute_components(expert_obs, expert_acts)\n        gen_d, gen_bc = self.reward_net.compute_components(gen_obs, gen_acts)\n\n        disc_loss = 0.5 * torch.mean((expert_d - 1.0)**2) + 0.5 * torch.mean((gen_d - (-1.0))**2)\n        bc_loss = F.mse_loss(expert_bc, expert_acts)\n\n        alpha = torch.rand(batch_size, 1, device=self.device)\n        interp_obs = (alpha * expert_obs + (1 - alpha) * gen_obs).requires_grad_(True)\n        interp_acts = (alpha * expert_acts + (1 - alpha) * gen_acts).requires_grad_(True)\n        \n        h_interp = self.reward_net.state_net(interp_obs)\n        d_interp = self.reward_net.disc_head(torch.cat([h_interp, interp_acts], dim=-1)).squeeze(-1)\n        \n        ones = torch.ones_like(d_interp)\n        grads = torch.autograd.grad(\n            outputs=d_interp, inputs=[interp_obs, interp_acts],\n            grad_outputs=ones, create_graph=True, retain_graph=True,\n        )\n        grad_cat = torch.cat([g.reshape(batch_size, -1) for g in grads], dim=1)\n        grad_norm = torch.sqrt(torch.sum(grad_cat ** 2, dim=1) + 1e-12)\n        gp = torch.mean((grad_norm - 1.0) ** 2)\n\n        loss = disc_loss + self.grad_penalty_coef * gp + 10.0 * bc_loss\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\n        p_bc_loss_val = 0.0\n        if self._policy is not None:\n            n_bc_steps = 10\n            for _ in range(n_bc_steps):\n                idx = torch.randint(0, n_expert, (batch_size,))\n                exp_o = self.expert_demos[\"obs\"][idx]\n                exp_a = self.expert_demos[\"acts\"][idx]\n                \n                _, log_prob, entropy, _ = self._policy.get_action_and_value(exp_o, exp_a)\n                p_bc_loss = -log_prob.mean() - 0.001 * entropy.mean()\n                \n                self._policy_optimizer.zero_grad()\n                p_bc_loss.backward()\n                nn.utils.clip_grad_norm_(self._policy.parameters(), 0.5)\n                self._policy_optimizer.step()\n                p_bc_loss_val += p_bc_loss.item()\n            p_bc_loss_val /= n_bc_steps\n\n        return {\n            \"irl_loss\": loss.item(),\n            \"disc_loss\": disc_loss.item(),\n            \"bc_loss\": bc_loss.item(),\n            \"p_bc_loss\": p_bc_loss_val\n        }"
7}

Tool ResultStep 2

OK: Replaced lines 231..357 with 129 line(s) in imitation/custom_irl.py. Editable range: 231–359.

[Current file: imitation/custom_irl.py | editable: 231–359 | total: 601 lines]
   231  class RewardNetwork(nn.Module):
   232      """Multi-Task Reward Network with shared state features."""
   233  
       ... (123 more lines) ...
   357              "bc_loss": bc_loss.item(),
   358              "p_bc_loss": p_bc_loss_val
   359          }

AssistantStep 3test

Tool ResultStep 3

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### halfcheetah-v4 (scripts/halfcheetah.sh)
Training metrics (last steps):
TRAIN_METRICS step=993280 pg_loss=0.3003 v_loss=15.8558 entropy=-10.8984 irl_loss=1.2236 disc_loss=0.8169 bc_loss=0.0115 p_bc_loss=-11.7359
TRAIN_METRICS step=995328 pg_loss=0.2950 v_loss=17.1726 entropy=-10.8995 irl_loss=1.3185 disc_loss=0.8744 bc_loss=0.0125 p_bc_loss=-11.7414
TRAIN_METRICS step=997376 pg_loss=0.3046 v_loss=20.0570 entropy=-10.9221 irl_loss=1.3204 disc_loss=0.8921 bc_loss=0.0132 p_bc_loss=-11.8087
TRAIN_METRICS step=999424 pg_loss=0.3025 v_loss=24.9686 entropy=-10.8659 irl_loss=1.0573 disc_loss=0.6147 bc_loss=0.0122 p_bc_loss=-11.7211
TRAIN_METRICS step=1000000 pg_loss=0.3048 v_loss=53.1911 entropy=-10.9230 irl_loss=1.5116 disc_loss=1.0351 bc_loss=0.0128 p_bc_loss=-11.7419
Evaluation (halfcheetah-v4):
Eval episodic_return: 3198.03
Eval episodic_return: 3532.08
Eval episodic_return: 3419.66
Final eva

AssistantStep 4edit

Tool Input

1{
2  "content": "class RewardNetwork(nn.Module):\n    \"\"\"AIRL shaped reward network: f(s,a,s') = g(s,a) + gamma * h(s') - h(s).\"\"\"\n\n    def __init__(self, obs_dim, action_dim):\n        super().__init__()\n        self.g_net = nn.Sequential(\n            nn.Linear(obs_dim + action_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n        )\n        self.h_net = nn.Sequential(\n            nn.Linear(obs_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n        )\n        self.gamma = 0.99\n\n    def g(self, state, action):\n        x = torch.cat([state, action], dim=-1)\n        return self.g_net(x).squeeze(-1)\n\n    def h(self, state):\n        return self.h_net(state).squeeze(-1)\n\n    def forward(self, state, action, next_state):\n        return self.g(state, action) + self.gamma * self.h(next_state) - self.h(state)\n\n\nclass IRLAlgorithm:\n    \"\"\"Demo-Regularized LS-AIRL (Least-Squares AIRL + BC).\"\"\"\n\n    def __init__(self, reward_net, expert_demos, obs_dim, action_dim, device, args):\n        self.reward_net = reward_net\n        self.expert_demos = expert_demos\n        self.device = device\n        self.args = args\n        self.obs_dim = obs_dim\n        self.action_dim = action_dim\n\n        self.optimizer = optim.Adam(self.reward_net.parameters(), lr=args.irl_lr)\n        self.total_updates = 0\n        self.grad_penalty_coef = 10.0\n\n        self._policy = None\n        self._policy_optimizer = None\n\n    def set_policy(self, policy, optimizer):\n        self._policy = policy\n        self._policy_optimizer = optimizer\n\n    def compute_reward(self, obs, acts, next_obs):\n        with torch.no_grad():\n            return self.reward_net(obs, acts, next_obs)\n\n    def _gradient_penalty(self, expert_obs, expert_acts, expert_next_obs,\n                          gen_obs, gen_acts, gen_next_obs):\n        batch_size = expert_obs.shape[0]\n        alpha = torch.rand(batch_size, 1, device=self.device)\n\n        interp_obs = (alpha * expert_obs + (1 - alpha) * gen_obs).requires_grad_(True)\n        interp_acts = (alpha * expert_acts + (1 - alpha) * gen_acts).requires_grad_(True)\n        interp_next = (alpha * expert_next_obs + (1 - alpha) * gen_next_obs).requires_grad_(True)\n\n        logits = self.reward_net(interp_obs, interp_acts, interp_next)\n        ones = torch.ones_like(logits)\n\n        grads = torch.autograd.grad(\n            outputs=logits, inputs=[interp_obs, interp_acts, interp_next],\n            grad_outputs=ones, create_graph=True, retain_graph=True,\n        )\n        grad_cat = torch.cat([g.reshape(batch_size, -1) for g in grads], dim=1)\n        grad_norm = torch.sqrt(torch.sum(grad_cat ** 2, dim=1) + 1e-12)\n        return torch.mean((grad_norm - 1.0) ** 2)\n\n    def update(self, policy_obs, policy_acts, policy_next_obs, policy_dones):\n        self.total_updates += 1\n        batch_size = self.args.irl_batch_size\n\n        n_expert = len(self.expert_demos[\"obs\"])\n        expert_idx = torch.randint(0, n_expert, (batch_size,))\n        expert_obs = self.expert_demos[\"obs\"][expert_idx]\n        expert_acts = self.expert_demos[\"acts\"][expert_idx]\n        expert_next_obs = self.expert_demos[\"next_obs\"][expert_idx]\n\n        n_policy = len(policy_obs)\n        policy_idx = torch.randint(0, n_policy, (batch_size,))\n        gen_obs = policy_obs[policy_idx]\n        gen_acts = policy_acts[policy_idx]\n        gen_next_obs = policy_next_obs[policy_idx]\n\n        expert_logits = self.reward_net(expert_obs, expert_acts, expert_next_obs)\n        gen_logits = self.reward_net(gen_obs, gen_acts, gen_next_obs)\n\n        # LSGAN Loss\n        disc_loss = 0.5 * torch.mean((expert_logits - 1.0)**2) + 0.5 * torch.mean((gen_logits - (-1.0))**2)\n\n        gp = self._gradient_penalty(\n            expert_obs, expert_acts, expert_next_obs,\n            gen_obs, gen_acts, gen_next_obs,\n        )\n\n        loss = disc_loss + self.grad_penalty_coef * gp\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\n        # Demo-Guided Policy Regularization (BC)\n        p_bc_loss_val = 0.0\n        if self._policy is not None:\n            n_bc_steps = 20\n            for _ in range(n_bc_steps):\n                idx = torch.randint(0, n_expert, (batch_size,))\n                exp_o = self.expert_demos[\"obs\"][idx]\n                exp_a = self.expert_demos[\"acts\"][idx]\n\n                _, log_prob, entropy, _ = self._policy.get_action_and_value(exp_o, exp_a)\n                p_bc_loss = -log_prob.mean() - 0.001 * entropy.mean()\n\n                self._policy_optimizer.zero_grad()\n                p_bc_loss.backward()\n                nn.utils.clip_grad_norm_(self._policy.parameters(), 0.5)\n                self._policy_optimizer.step()\n                p_bc_loss_val += p_bc_loss.item()\n            p_bc_loss_val /= n_bc_steps\n\n        return {\n            \"irl_loss\": loss.item(),\n            \"grad_penalty\": gp.item(),\n            \"p_bc_loss\": p_bc_loss_val\n        }",
3  "start_line": 231,
4  "end_line": 359,
5  "op": "replace",
6  "filename": "imitation/custom_irl.py"
7}

Tool ResultStep 4

OK: Replaced lines 231..359 with 135 line(s) in imitation/custom_irl.py. Editable range: 231–365.

[Current file: imitation/custom_irl.py | editable: 231–365 | total: 607 lines]
   231  class RewardNetwork(nn.Module):
   232      """AIRL shaped reward network: f(s,a,s') = g(s,a) + gamma * h(s') - h(s)."""
   233  
       ... (129 more lines) ...
   363              "grad_penalty": gp.item(),
   364              "p_bc_loss": p_bc_loss_val
   365          }

AssistantStep 5test

Tool ResultStep 5

[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### halfcheetah-v4 (scripts/halfcheetah.sh)
Training metrics (last steps):
TRAIN_METRICS step=993280 pg_loss=0.3114 v_loss=41.3663 entropy=-13.3465 irl_loss=0.8699 grad_penalty=0.0012 p_bc_loss=-13.7833
TRAIN_METRICS step=995328 pg_loss=0.3053 v_loss=22.9781 entropy=-13.3245 irl_loss=1.0233 grad_penalty=0.0013 p_bc_loss=-13.7528
TRAIN_METRICS step=997376 pg_loss=0.3080 v_loss=23.4172 entropy=-13.3865 irl_loss=1.0218 grad_penalty=0.0012 p_bc_loss=-13.8431
TRAIN_METRICS step=999424 pg_loss=0.3012 v_loss=17.5125 entropy=-13.4157 irl_loss=0.9965 grad_penalty=0.0011 p_bc_loss=-13.7837
TRAIN_METRICS step=1000000 pg_loss=0.3019 v_loss=19.5665 entropy=-13.4646 irl_loss=1.0194 grad_penalty=0.0014 p_bc_loss=-13.9039
Evaluation (halfcheetah-v4):
Eval episodic_return: 3338.48
Eval episodic_return: 3272.54
Eval episodic_return: 2969.47
Final eval return: 2969.47

### hopper-v4 (scripts/hopper.sh)
Training

AssistantStep 6edit

Tool Input

1{
2  "op": "replace",
3  "end_line": 365,
4  "start_line": 231,
5  "content": "class RewardNetwork(nn.Module):\n    \"\"\"LS-AIRL shaped reward network: f(s,a,s') = g(s,a) + gamma * h(s') - h(s).\"\"\"\n\n    def __init__(self, obs_dim, action_dim):\n        super().__init__()\n        self.g_net = nn.Sequential(\n            nn.Linear(obs_dim + action_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n        )\n        self.h_net = nn.Sequential(\n            nn.Linear(obs_dim, 256),\n            nn.ReLU(),\n            nn.Linear(256, 256),\n            nn.ReLU(),\n            nn.Linear(256, 1),\n        )\n        self.gamma = 0.99\n\n    def g(self, state, action):\n        x = torch.cat([state, action], dim=-1)\n        return self.g_net(x).squeeze(-1)\n\n    def h(self, state):\n        return self.h_net(state).squeeze(-1)\n\n    def forward(self, state, action, next_state):\n        return self.g(state, action) + self.gamma * self.h(next_state) - self.h(state)\n\n\nclass IRLAlgorithm:\n    \"\"\"Demo-Regularized LS-AIRL (Least-Squares AIRL + BC on Mean).\"\"\"\n\n    def __init__(self, reward_net, expert_demos, obs_dim, action_dim, device, args):\n        self.reward_net = reward_net\n        self.expert_demos = expert_demos\n        self.device = device\n        self.args = args\n        self.obs_dim = obs_dim\n        self.action_dim = action_dim\n\n        self.optimizer = optim.Adam(self.reward_net.parameters(), lr=args.irl_lr)\n        self.total_updates = 0\n        self.grad_penalty_coef = 10.0\n\n        self._policy = None\n        self._policy_optimizer = None\n\n    def set_policy(self, policy, optimizer):\n        self._policy = policy\n        self._policy_optimizer = optimizer\n\n    def compute_reward(self, obs, acts, next_obs):\n        with torch.no_grad():\n            return self.reward_net(obs, acts, next_obs)\n\n    def _gradient_penalty(self, expert_obs, expert_acts, expert_next_obs,\n                          gen_obs, gen_acts, gen_next_obs):\n        batch_size = expert_obs.shape[0]\n        alpha = torch.rand(batch_size, 1, device=self.device)\n\n        interp_obs = (alpha * expert_obs + (1 - alpha) * gen_obs).requires_grad_(True)\n        interp_acts = (alpha * expert_acts + (1 - alpha) * gen_acts).requires_grad_(True)\n        interp_next = (alpha * expert_next_obs + (1 - alpha) * gen_next_obs).requires_grad_(True)\n\n        logits = self.reward_net(interp_obs, interp_acts, interp_next)\n        ones = torch.ones_like(logits)\n\n        grads = torch.autograd.grad(\n            outputs=logits, inputs=[interp_obs, interp_acts, interp_next],\n            grad_outputs=ones, create_graph=True, retain_graph=True,\n        )\n        grad_cat = torch.cat([g.reshape(batch_size, -1) for g in grads], dim=1)\n        grad_norm = torch.sqrt(torch.sum(grad_cat ** 2, dim=1) + 1e-12)\n        return torch.mean((grad_norm - 1.0) ** 2)\n\n    def update(self, policy_obs, policy_acts, policy_next_obs, policy_dones):\n        self.total_updates += 1\n        batch_size = self.args.irl_batch_size\n\n        n_expert = len(self.expert_demos[\"obs\"])\n        expert_idx = torch.randint(0, n_expert, (batch_size,))\n        expert_obs = self.expert_demos[\"obs\"][expert_idx]\n        expert_acts = self.expert_demos[\"acts\"][expert_idx]\n        expert_next_obs = self.expert_demos[\"next_obs\"][expert_idx]\n\n        n_policy = len(policy_obs)\n        policy_idx = torch.randint(0, n_policy, (batch_size,))\n        gen_obs = policy_obs[policy_idx]\n        gen_acts = policy_acts[policy_idx]\n        gen_next_obs = policy_next_obs[policy_idx]\n\n        expert_logits = self.reward_net(expert_obs, expert_acts, expert_next_obs)\n        gen_logits = self.reward_net(gen_obs, gen_acts, gen_next_obs)\n\n        # LSGAN Loss\n        disc_loss = 0.5 * torch.mean((expert_logits - 1.0)**2) + 0.5 * torch.mean((gen_logits - (-1.0))**2)\n\n        gp = self._gradient_penalty(\n            expert_obs, expert_acts, expert_next_obs,\n            gen_obs, gen_acts, gen_next_obs,\n        )\n\n        loss = disc_loss + self.grad_penalty_coef * gp\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\n        # Demo-Guided Policy Regularization (BC on Mean ONLY to prevent variance collapse)\n        p_bc_loss_val = 0.0\n        if self._policy is not None:\n            n_bc_steps = 20\n            for _ in range(n_bc_steps):\n                idx = torch.randint(0, n_expert, (batch_size,))\n                exp_o = self.expert_demos[\"obs\"][idx]\n                exp_a = self.expert_demos[\"acts\"][idx]\n\n                # Update only the mean to match expert actions, keeping logstd untouched\n                action_mean = self._policy.actor_mean(exp_o)\n                p_bc_loss = F.mse_loss(action_mean, exp_a)\n\n                self._policy_optimizer.zero_grad()\n                p_bc_loss.backward()\n                nn.utils.clip_grad_norm_(self._policy.parameters(), 0.5)\n                self._policy_optimizer.step()\n                p_bc_loss_val += p_bc_loss.item()\n            p_bc_loss_val /= n_bc_steps\n\n        return {\n            \"irl_loss\": loss.item(),\n            \"grad_penalty\": gp.item(),\n            \"p_bc_loss\": p_bc_loss_val\n        }",
6  "filename": "imitation/custom_irl.py"
7}

Tool ResultStep 6

OK: Replaced lines 231..365 with 136 line(s) in imitation/custom_irl.py. Editable range: 231–366.

[Current file: imitation/custom_irl.py | editable: 231–366 | total: 608 lines]
   231  class RewardNetwork(nn.Module):
   232      """LS-AIRL shaped reward network: f(s,a,s') = g(s,a) + gamma * h(s') - h(s)."""
   233  
       ... (130 more lines) ...
   364              "grad_penalty": gp.item(),
   365              "p_bc_loss": p_bc_loss_val
   366          }

AssistantStep 7test

Tool ResultStep 7

[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)

[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.

### halfcheetah-v4 (scripts/halfcheetah.sh)
Training metrics (last steps):
TRAIN_METRICS step=993280 pg_loss=-0.0010 v_loss=49.9868 entropy=-0.6299 irl_loss=0.6296 grad_penalty=0.0022 p_bc_loss=0.0009
TRAIN_METRICS step=995328 pg_loss=-0.0016 v_loss=83.0296 entropy=-0.6479 irl_loss=0.5901 grad_penalty=0.0020 p_bc_loss=0.0009
TRAIN_METRICS step=997376 pg_loss=-0.0122 v_loss=37.8522 entropy=-0.6711 irl_loss=0.6081 grad_penalty=0.0020 p_bc_loss=0.0008
TRAIN_METRICS step=999424 pg_loss=-0.0052 v_loss=58.0866 entropy=-0.6993 irl_loss=0.6023 grad_penalty=0.0018 p_bc_loss=0.0009
TRAIN_METRICS step=1000000 pg_loss=0.0383 v_loss=62.9409 entropy=-0.7076 irl_loss=0.6384 grad_penalty=0.0019 p_bc_loss=0.0008
Evaluation (halfcheetah-v4):
Eval episodic_return: 3008.39
Eval episodic_ret

AssistantStep 8submit

Tool Input

1{
2  "n": 3
3}

Tool ResultStep 8

[submit] Submitting result from test #3 as final.

### halfcheetah-v4 (scripts/halfcheetah.sh)
Training metrics (last steps):
TRAIN_METRICS step=993280 pg_loss=-0.0010 v_loss=49.9868 entropy=-0.6299 irl_loss=0.6296 grad_penalty=0.0022 p_bc_loss=0.0009
TRAIN_METRICS step=995328 pg_loss=-0.0016 v_loss=83.0296 entropy=-0.6479 irl_loss=0.5901 grad_penalty=0.0020 p_bc_loss=0.0009
TRAIN_METRICS step=997376 pg_loss=-0.0122 v_loss=37.8522 entropy=-0.6711 irl_loss=0.6081 grad_penalty=0.0020 p_bc_loss=0.0008
TRAIN_METRICS step=999424 pg_loss=-0.0052 v_loss=58.0866 entropy=-0.6993 irl_loss=0.6023 grad_penalty=0.0018 p_bc_loss=0.0009
TRAIN_METRICS step=1000000 pg_loss=0.0383 v_loss=62.9409 entropy=-0.7076 irl_loss=0.6384 grad_penalty=0.0019 p_bc_loss=0.0008
Evaluation (halfcheetah-v4):
Eval episodic_return: 3008.39
Eval episodic_return: 2991.91
Eval episodic_return: 3014.00
Final eval return: 3014.00

### hopper-v4 (scripts/hopper.sh)
Training metrics (last steps):
TRAIN_METRICS step=993280 pg_los