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Task: rl-intrinsic-exploration

RL Intrinsic Exploration: Sparse-Reward Novelty Bonus Design

Research Question

Design an intrinsic exploration mechanism that improves sparse-reward discovery in hard-exploration Atari environments.

Background

In sparse-reward reinforcement learning, external rewards arrive too infrequently for vanilla policy optimization to learn efficiently. A common solution is to add an intrinsic reward that encourages novelty, surprise, or state-space coverage.

This task isolates that question. The PPO training loop, Atari preprocessing, policy/value architecture, and optimization setup are fixed. The only thing you should redesign is the intrinsic bonus module and how its signal is mixed into learning.

Reference algorithm families include:

No bonus / vanilla PPO: learns only from clipped extrinsic reward
RND: rewards prediction error against a fixed random target network
ICM: rewards forward-dynamics prediction error in learned feature space

Task

Modify the editable section of custom_intrinsic_exploration.py:

IntrinsicBonusModule: define how intrinsic rewards are computed and trained
mix_advantages(...): define how extrinsic and intrinsic advantages are combined

The editable code must keep the public interface intact:

initialize(envs)
trainable_parameters()
update_batch_stats(batch_obs, batch_next_obs)
compute_bonus(obs, next_obs, actions)
normalize_rollout_rewards(rollout_intrinsic)
loss(batch_obs, batch_next_obs, batch_actions)
mix_advantages(ext_advantages, int_advantages, args)

Evaluation

The agent is trained with the same fixed PPO-style loop on three sparse-reward Atari environments:

Tutankham-v5
Frostbite-v5
PrivateEye-v5

Tutankham-v5 and Frostbite-v5 are visible during development. PrivateEye-v5 is held out as the hidden evaluation environment.

Metrics:

eval_return: mean evaluation episodic return at a fixed training budget
auc: area under the evaluation-return curve across training
nonzero_rate: fraction of evaluation episodes with non-zero episodic return

Evaluation uses deterministic rollouts with a fixed per-episode step cap so a non-terminating Atari behavior cannot stall the benchmark.

Improvement must transfer across multiple games; a method that only helps one environment is not sufficient. Tutankham-v5 is included as a medium-difficulty visible environment so baseline ranking is measurable at a modest training budget before transfer is checked on the harder visible and hidden games.

cleanrl/cleanrl/custom_intrinsic_exploration.py [EDITABLE — lines 179–219 only]

     1: # Custom sparse-reward Atari exploration benchmark for MLS-Bench.
     2: #
     3: # FIXED sections: PPO loop, Atari preprocessing, policy/value architecture,
     4: # evaluation, logging, and optimizer wiring.
     5: # EDITABLE section: IntrinsicBonusModule + mix_advantages.
     6: 
     7: from __fu

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "cleanrl/cleanrl/custom_intrinsic_exploration.py",
4  "start_line": 179,
5  "end_line": 219,
6  "content": "# =====================================================================\n# EDITABLE: intrinsic reward design\n# =====================================================================\nclass IntrinsicBonusModule(nn.Module):\n    \"\"\"Ensemble Dynamics Disagreement intrinsic bonus.\"\"\"\n\n    def __init__(self, action_dim: int, device: torch.device, args: Args):\n        super().__init__()\n        self.action_dim = action_dim\n        self.device = device\n        self.args = args\n        self.obs_rms = RunningMeanStd(shape=(1, 1, 84, 84))\n        self.reward_rms = RunningMeanStd()\n        self.discounted_reward = RewardForwardFilter(args.int_gamma)\n        \n        # Hyperparameters\n        self.ensemble_size = 5\n        self.feature_dim = 256\n        self.hidden_dim = 256\n        \n        # Shared encoder for observations\n        feature_output = 7 * 7 * 64\n        self.encoder = nn.Sequential(\n            layer_init(nn.Conv2d(1, 32, 8, stride=4)),\n            nn.ReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.ReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.ReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(feature_output, self.feature_dim)),\n            nn.ReLU(),\n        )\n        \n        # Ensemble of forward dynamics predictors\n        self.forward_models = nn.ModuleList()\n        for _ in range(self.ensemble_size):\n            model = nn.Sequential(\n                layer_init(nn.Linear(self.feature_dim + action_dim, self.hidden_dim)),\n                nn.ReLU(),\n                layer_init(nn.Linear(self.hidden_dim, self.hidden_dim)),\n                nn.ReLU(),\n                layer_init(nn.Linear(self.hidden_dim, self.feature_dim)),\n            )\n            self.forward_models.append(model)\n\n    def initialize(self, envs) -> None:\n        bootstrap = []\n        total_steps = self.args.num_steps * self.args.num_iterations_obs_norm_init\n        for _ in range(total_steps):\n            random_actions = np.random.randint(0, envs.single_action_space.n, size=(self.args.num_envs,))\n            sampled_obs, _, _, _ = envs.step(random_actions)\n            bootstrap.append(sampled_obs[:, 3:4, :, :])\n            if len(bootstrap) >= self.args.num_steps:\n                stacked = np.concatenate(bootstrap, axis=0)\n                self.obs_rms.update(stacked)\n                bootstrap.clear()\n\n    def trainable_parameters(self):\n        return list(self.parameters())\n\n    def _normalize_obs(self, obs: torch.Tensor) -> torch.Tensor:\n        mean = torch.from_numpy(self.obs_rms.mean).to(self.device)\n        var = torch.from_numpy(self.obs_rms.var).to(self.device)\n        return ((last_frame(obs) - mean) / torch.sqrt(var)).clip(-5, 5).float()\n\n    def _one_hot(self, actions: torch.Tensor) -> torch.Tensor:\n        return F.one_hot(actions.long(), num_classes=self.action_dim).float()\n\n    def update_batch_stats(self, batch_obs: torch.Tensor, batch_next_obs: torch.Tensor) -> None:\n        self.obs_rms.update(last_frame(batch_next_obs).cpu().numpy())\n\n    def compute_bonus(\n        self,\n        obs: torch.Tensor,\n        next_obs: torch.Tensor,\n        actions: torch.Tensor,\n    ) -> torch.Tensor:\n        # Encode current and next observations\n        obs_feat = self.encoder(self._normalize_obs(obs))\n        next_feat = self.encoder(self._normalize_obs(next_obs))\n        \n        # Get predictions from each ensemble member\n        predictions = []\n        for model in self.forward_models:\n            pred = model(torch.cat([obs_feat, self._one_hot(actions)], dim=1))\n            predictions.append(pred)\n        \n        # Stack predictions: [ensemble_size, batch_size, feature_dim]\n        pred_stack = torch.stack(predictions, dim=0)\n        \n        # Compute variance across ensemble members\n        variance = torch.var(pred_stack, dim=0)  # [batch_size, feature_dim]\n        \n        # Intrinsic reward = mean variance across feature dimensions\n        bonus = variance.mean(dim=1)  # [batch_size]\n        \n        return bonus.detach()\n\n    def normalize_rollout_rewards(self, rollout_intrinsic: torch.Tensor) -> torch.Tensor:\n        discounted = np.stack(\n            [self.discounted_reward.update(reward_per_step) for reward_per_step in rollout_intrinsic.cpu().numpy()],\n            axis=0,\n        )\n        flat_discounted = discounted.reshape(-1)\n        self.reward_rms.update_from_moments(\n            float(flat_discounted.mean()),\n            float(flat_discounted.var()),\n            int(flat_discounted.size),\n        )\n        return rollout_intrinsic / float(np.sqrt(self.reward_rms.var + 1e-8))\n\n    def loss(\n        self,\n        batch_obs: torch.Tensor,\n        batch_next_obs: torch.Tensor,\n        batch_actions: torch.Tensor,\n    ) -> torch.Tensor:\n        # Encode observations\n        obs_feat = self.encoder(self._normalize_obs(batch_obs))\n        next_feat = self.encoder(self._normalize_obs(batch_next_obs)).detach()\n        \n        # Compute prediction loss for each ensemble member\n        total_loss = 0.0\n        for model in self.forward_models:\n            pred = model(torch.cat([obs_feat, self._one_hot(batch_actions)], dim=1))\n            # MSE loss between predicted and target features\n            loss = F.mse_loss(pred, next_feat, reduction='mean')\n            total_loss += loss\n        \n        # Average loss across ensemble\n        return total_loss / self.ensemble_size\n\n\ndef mix_advantages(ext_advantages: torch.Tensor, int_advantages: torch.Tensor, args: Args) -> torch.Tensor:\n    return args.ext_coef * ext_advantages + args.int_coef * int_advantages"
7}

Tool ResultStep 1

OK: Replaced lines 179..219 with 136 line(s) in cleanrl/cleanrl/custom_intrinsic_exploration.py. Editable range: 179–314.

[Current file: cleanrl/cleanrl/custom_intrinsic_exploration.py | editable: 179–314 | total: 587 lines]
   179  # =====================================================================
   180  # EDITABLE: intrinsic reward design
   181  # =====================================================================
       ... (130 more lines) ...
   312  
   313  def mix_advantages(ext_advantages: torch.Tensor, int_advantages: torch.Tensor, args: Args) -> torch.Tensor:
   314      return args.ext_coef * ext_advantages + args.int_coef * int_advantages

AssistantStep 2test

Tool ResultStep 2

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

### tutankham-v5 (scripts/tutankham.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1371
TRAIN_METRICS step=9986048 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1372
TRAIN_METRICS step=9990144 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1372
TRAIN_METRICS step=9994240 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1372
TRAIN_METRICS step=9998336 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=0.0000 nonzero_rate=0.0000 sps=1337
Final evaluation:
TEST_METRICS eval_return=0.0000 auc=0.000000 nonzero_rate=0.0000 best_eval_return=0.0000

### frostbite-v5 (scripts/frostbite.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=260.0000 avg_intrinsic=0.000000 eval_return=nan nonzero

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "cleanrl/cleanrl/custom_intrinsic_exploration.py",
4  "start_line": 179,
5  "end_line": 314,
6  "content": "# =====================================================================\n# EDITABLE: intrinsic reward design\n# =====================================================================\nclass IntrinsicBonusModule(nn.Module):\n    \"\"\"RND with Episodic Memory intrinsic bonus.\"\"\"\n\n    def __init__(self, action_dim: int, device: torch.device, args: Args):\n        super().__init__()\n        self.action_dim = action_dim\n        self.device = device\n        self.args = args\n        self.obs_rms = RunningMeanStd(shape=(1, 1, 84, 84))\n        self.reward_rms = RunningMeanStd()\n        self.discounted_reward = RewardForwardFilter(args.int_gamma)\n        \n        # Hyperparameters\n        self.memory_size = 5000\n        self.k_neighbors = 10\n        self.episodic_coef = 0.1\n        self.rnd_coef = 1.0\n        \n        # Episodic memory: store random features of visited states\n        self.memory = deque(maxlen=self.memory_size)\n        \n        feature_output = 7 * 7 * 64\n        # RND predictor network (trainable)\n        self.predictor = nn.Sequential(\n            layer_init(nn.Conv2d(1, 32, 8, stride=4)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.LeakyReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(feature_output, 512)),\n            nn.ReLU(),\n            layer_init(nn.Linear(512, 512)),\n            nn.ReLU(),\n            layer_init(nn.Linear(512, 512)),\n        )\n        # RND target network (fixed random)\n        self.target = nn.Sequential(\n            layer_init(nn.Conv2d(1, 32, 8, stride=4)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.LeakyReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(feature_output, 512)),\n        )\n        for param in self.target.parameters():\n            param.requires_grad = False\n\n    def initialize(self, envs) -> None:\n        bootstrap = []\n        total_steps = self.args.num_steps * self.args.num_iterations_obs_norm_init\n        for _ in range(total_steps):\n            random_actions = np.random.randint(0, envs.single_action_space.n, size=(self.args.num_envs,))\n            sampled_obs, _, _, _ = envs.step(random_actions)\n            bootstrap.append(sampled_obs[:, 3:4, :, :])\n            if len(bootstrap) >= self.args.num_steps:\n                stacked = np.concatenate(bootstrap, axis=0)\n                self.obs_rms.update(stacked)\n                bootstrap.clear()\n\n    def trainable_parameters(self):\n        return list(self.predictor.parameters())\n\n    def _normalize_obs(self, obs: torch.Tensor) -> torch.Tensor:\n        mean = torch.from_numpy(self.obs_rms.mean).to(self.device)\n        var = torch.from_numpy(self.obs_rms.var).to(self.device)\n        return ((last_frame(obs) - mean) / torch.sqrt(var)).clip(-5, 5).float()\n\n    def update_batch_stats(self, batch_obs: torch.Tensor, batch_next_obs: torch.Tensor) -> None:\n        self.obs_rms.update(last_frame(batch_next_obs).cpu().numpy())\n\n    def compute_bonus(\n        self,\n        obs: torch.Tensor,\n        next_obs: torch.Tensor,\n        actions: torch.Tensor,\n    ) -> torch.Tensor:\n        norm_next = self._normalize_obs(next_obs)\n        \n        # RND bonus: prediction error\n        target_feature = self.target(norm_next)\n        predict_feature = self.predictor(norm_next)\n        rnd_bonus = ((target_feature - predict_feature).pow(2).sum(1) / 2).detach()\n        \n        # Episodic bonus: distance to nearest neighbors in memory\n        with torch.no_grad():\n            target_feature_np = target_feature.cpu().numpy()\n            if len(self.memory) > self.k_neighbors:\n                # Convert memory to array\n                mem_array = np.array(list(self.memory))\n                # Compute distances (batch_size, memory_size)\n                dists = np.sum((target_feature_np[:, None, :] - mem_array[None, :, :]) ** 2, axis=2)\n                # Find k nearest distances\n                knn_dists = np.partition(dists, kth=self.k_neighbors-1, axis=1)[:, :self.k_neighbors]\n                episodic_bonus = np.mean(knn_dists, axis=1)\n            else:\n                episodic_bonus = np.ones(target_feature_np.shape[0])\n        \n        episodic_bonus = torch.from_numpy(episodic_bonus).float().to(self.device)\n        \n        # Update memory with target features\n        for feat in target_feature_np:\n            self.memory.append(feat)\n        \n        # Combined bonus\n        bonus = self.rnd_coef * rnd_bonus + self.episodic_coef * episodic_bonus\n        return bonus\n\n    def normalize_rollout_rewards(self, rollout_intrinsic: torch.Tensor) -> torch.Tensor:\n        discounted = np.stack(\n            [self.discounted_reward.update(reward_per_step) for reward_per_step in rollout_intrinsic.cpu().numpy()],\n            axis=0,\n        )\n        flat_discounted = discounted.reshape(-1)\n        self.reward_rms.update_from_moments(\n            float(flat_discounted.mean()),\n            float(flat_discounted.var()),\n            int(flat_discounted.size),\n        )\n        return rollout_intrinsic / float(np.sqrt(self.reward_rms.var + 1e-8))\n\n    def loss(\n        self,\n        batch_obs: torch.Tensor,\n        batch_next_obs: torch.Tensor,\n        batch_actions: torch.Tensor,\n    ) -> torch.Tensor:\n        norm_next = self._normalize_obs(batch_next_obs)\n        predict_feature = self.predictor(norm_next)\n        target_feature = self.target(norm_next).detach()\n        forward_loss = F.mse_loss(predict_feature, target_feature, reduction=\"none\").mean(-1)\n        mask = (torch.rand(len(forward_loss), device=self.device) < self.args.update_proportion).float()\n        return (forward_loss * mask).sum() / torch.clamp(mask.sum(), min=1.0)\n\n\ndef mix_advantages(ext_advantages: torch.Tensor, int_advantages: torch.Tensor, args: Args) -> torch.Tensor:\n    return args.ext_coef * ext_advantages + args.int_coef * int_advantages"
7}

Tool ResultStep 3

OK: Replaced lines 179..314 with 143 line(s) in cleanrl/cleanrl/custom_intrinsic_exploration.py. Editable range: 179–321.

[Current file: cleanrl/cleanrl/custom_intrinsic_exploration.py | editable: 179–321 | total: 594 lines]
   179  # =====================================================================
   180  # EDITABLE: intrinsic reward design
   181  # =====================================================================
       ... (137 more lines) ...
   319  
   320  def mix_advantages(ext_advantages: torch.Tensor, int_advantages: torch.Tensor, args: Args) -> torch.Tensor:
   321      return args.ext_coef * ext_advantages + args.int_coef * int_advantages

AssistantStep 4test

Tool ResultStep 4

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

### tutankham-v5 (scripts/tutankham.sh)
Training metrics (last steps):
TRAIN_METRICS step=2715648 avg_return=109.1000 avg_intrinsic=0.001810 eval_return=nan nonzero_rate=nan sps=95
TRAIN_METRICS step=2719744 avg_return=107.8500 avg_intrinsic=0.001500 eval_return=nan nonzero_rate=nan sps=95
TRAIN_METRICS step=2723840 avg_return=107.9000 avg_intrinsic=0.002252 eval_return=nan nonzero_rate=nan sps=95
TRAIN_METRICS step=2727936 avg_return=106.5500 avg_intrinsic=0.001654 eval_return=nan nonzero_rate=nan sps=95
TRAIN_METRICS step=2732032 avg_return=106.4000 avg_intrinsic=0.001586 eval_return=nan nonzero_rate=nan sps=95

### frostbite-v5 (scripts/frostbite.sh)
Training metrics (last steps):
TRAIN_METRICS step=2682880 avg_return=244.5000 avg_intrinsic=0.014342 eval_return=nan nonzero_rate=nan sps=93
TRAIN_METRICS step=2686976 avg_return=244.5000 avg_intrinsic=0.016734 eval_return=nan nonzero_rate=nan

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "cleanrl/cleanrl/custom_intrinsic_exploration.py",
4  "start_line": 179,
5  "end_line": 321,
6  "content": "# =====================================================================\n# EDITABLE: intrinsic reward design\n# =====================================================================\nclass IntrinsicBonusModule(nn.Module):\n    \"\"\"Hashed Random Network Distillation with count-based scaling.\"\"\"\n\n    def __init__(self, action_dim: int, device: torch.device, args: Args):\n        super().__init__()\n        self.action_dim = action_dim\n        self.device = device\n        self.args = args\n        self.obs_rms = RunningMeanStd(shape=(1, 1, 84, 84))\n        self.reward_rms = RunningMeanStd()\n        self.discounted_reward = RewardForwardFilter(args.int_gamma)\n        \n        # Hyperparameters\n        self.projection_dim = 16\n        self.bins_per_dim = 10\n        self.count_clip = 100\n        \n        # Count dictionary: map hash string to count\n        self.counts = {}\n        self.total_visits = 0\n        \n        feature_output = 7 * 7 * 64\n        # RND predictor network (trainable)\n        self.predictor = nn.Sequential(\n            layer_init(nn.Conv2d(1, 32, 8, stride=4)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.LeakyReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(feature_output, 512)),\n            nn.ReLU(),\n            layer_init(nn.Linear(512, 512)),\n            nn.ReLU(),\n            layer_init(nn.Linear(512, 512)),\n        )\n        # RND target network (fixed random)\n        self.target = nn.Sequential(\n            layer_init(nn.Conv2d(1, 32, 8, stride=4)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.LeakyReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.LeakyReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(feature_output, 512)),\n        )\n        for param in self.target.parameters():\n            param.requires_grad = False\n        \n        # Random projection matrix: project 512-dim feature to projection_dim\n        self.register_buffer('proj_matrix', torch.randn(512, self.projection_dim) * 0.1)\n        # Bin boundaries: assume features are roughly zero-mean, unit variance\n        self.bin_boundaries = torch.linspace(-2.0, 2.0, steps=self.bins_per_dim + 1, device=device)\n\n    def initialize(self, envs) -> None:\n        bootstrap = []\n        total_steps = self.args.num_steps * self.args.num_iterations_obs_norm_init\n        for _ in range(total_steps):\n            random_actions = np.random.randint(0, envs.single_action_space.n, size=(self.args.num_envs,))\n            sampled_obs, _, _, _ = envs.step(random_actions)\n            bootstrap.append(sampled_obs[:, 3:4, :, :])\n            if len(bootstrap) >= self.args.num_steps:\n                stacked = np.concatenate(bootstrap, axis=0)\n                self.obs_rms.update(stacked)\n                bootstrap.clear()\n\n    def trainable_parameters(self):\n        return list(self.predictor.parameters())\n\n    def _normalize_obs(self, obs: torch.Tensor) -> torch.Tensor:\n        mean = torch.from_numpy(self.obs_rms.mean).to(self.device)\n        var = torch.from_numpy(self.obs_rms.var).to(self.device)\n        return ((last_frame(obs) - mean) / torch.sqrt(var)).clip(-5, 5).float()\n\n    def update_batch_stats(self, batch_obs: torch.Tensor, batch_next_obs: torch.Tensor) -> None:\n        self.obs_rms.update(last_frame(batch_next_obs).cpu().numpy())\n\n    def _compute_hash(self, features: torch.Tensor) -> list:\n        \"\"\"Convert batch of features to list of hash strings.\"\"\"\n        # Project to lower dimension\n        projected = torch.mm(features, self.proj_matrix)  # [batch, projection_dim]\n        # Discretize each dimension\n        bins = torch.bucketize(projected, self.bin_boundaries)  # [batch, projection_dim]\n        # Convert to hash strings\n        hash_list = []\n        for i in range(bins.shape[0]):\n            key = tuple(bins[i].cpu().numpy())\n            hash_list.append(str(key))\n        return hash_list\n\n    def compute_bonus(\n        self,\n        obs: torch.Tensor,\n        next_obs: torch.Tensor,\n        actions: torch.Tensor,\n    ) -> torch.Tensor:\n        norm_next = self._normalize_obs(next_obs)\n        \n        # RND bonus: prediction error\n        target_feature = self.target(norm_next)  # [batch, 512]\n        predict_feature = self.predictor(norm_next)\n        rnd_bonus = ((target_feature - predict_feature).pow(2).sum(1) / 2).detach()  # [batch]\n        \n        # Count-based scaling: compute hash of target feature\n        with torch.no_grad():\n            hash_list = self._compute_hash(target_feature)\n            count_bonus = []\n            for h in hash_list:\n                cnt = self.counts.get(h, 0)\n                # Update count (clipped)\n                self.counts[h] = min(cnt + 1, self.count_clip)\n                # Scaling factor: inverse square root of count\n                scale = 1.0 / (np.sqrt(cnt + 1))\n                count_bonus.append(scale)\n            count_bonus = torch.tensor(count_bonus, device=self.device, dtype=torch.float32)\n        \n        # Combined bonus: RND error weighted by novelty\n        bonus = rnd_bonus * count_bonus\n        return bonus\n\n    def normalize_rollout_rewards(self, rollout_intrinsic: torch.Tensor) -> torch.Tensor:\n        discounted = np.stack(\n            [self.discounted_reward.update(reward_per_step) for reward_per_step in rollout_intrinsic.cpu().numpy()],\n            axis=0,\n        )\n        flat_discounted = discounted.reshape(-1)\n        self.reward_rms.update_from_moments(\n            float(flat_discounted.mean()),\n            float(flat_discounted.var()),\n            int(flat_discounted.size),\n        )\n        return rollout_intrinsic / float(np.sqrt(self.reward_rms.var + 1e-8))\n\n    def loss(\n        self,\n        batch_obs: torch.Tensor,\n        batch_next_obs: torch.Tensor,\n        batch_actions: torch.Tensor,\n    ) -> torch.Tensor:\n        norm_next = self._normalize_obs(batch_next_obs)\n        predict_feature = self.predictor(norm_next)\n        target_feature = self.target(norm_next).detach()\n        forward_loss = F.mse_loss(predict_feature, target_feature, reduction=\"none\").mean(-1)\n        mask = (torch.rand(len(forward_loss), device=self.device) < self.args.update_proportion).float()\n        return (forward_loss * mask).sum() / torch.clamp(mask.sum(), min=1.0)\n\n\ndef mix_advantages(ext_advantages: torch.Tensor, int_advantages: torch.Tensor, args: Args) -> torch.Tensor:\n    return args.ext_coef * ext_advantages + args.int_coef * int_advantages"
7}

Tool ResultStep 5

OK: Replaced lines 179..321 with 154 line(s) in cleanrl/cleanrl/custom_intrinsic_exploration.py. Editable range: 179–332.

[Current file: cleanrl/cleanrl/custom_intrinsic_exploration.py | editable: 179–332 | total: 605 lines]
   179  # =====================================================================
   180  # EDITABLE: intrinsic reward design
   181  # =====================================================================
       ... (148 more lines) ...
   330  
   331  def mix_advantages(ext_advantages: torch.Tensor, int_advantages: torch.Tensor, args: Args) -> torch.Tensor:
   332      return args.ext_coef * ext_advantages + args.int_coef * int_advantages

AssistantStep 6test

Tool ResultStep 6

[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.

### tutankham-v5 (scripts/tutankham.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=130.4500 avg_intrinsic=0.000943 eval_return=nan nonzero_rate=nan sps=881
TRAIN_METRICS step=9986048 avg_return=130.4500 avg_intrinsic=0.001109 eval_return=nan nonzero_rate=nan sps=881
TRAIN_METRICS step=9990144 avg_return=130.4500 avg_intrinsic=0.001515 eval_return=nan nonzero_rate=nan sps=881
TRAIN_METRICS step=9994240 avg_return=131.2500 avg_intrinsic=0.001152 eval_return=nan nonzero_rate=nan sps=882
TRAIN_METRICS step=9998336 avg_return=132.0500 avg_intrinsic=0.000747 eval_return=104.4000 nonzero_rate=1.0000 sps=859
Final evaluation:
TEST_METRICS eval_return=104.4000 auc=49.609785 nonzero_rate=1.0000 best_eval_return=106.2000

### frostbite-v5 (scripts/frostbit

AssistantStep 7submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 7

[submit] Submitting result from test #1 as final.

### tutankham-v5 (scripts/tutankham.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1371
TRAIN_METRICS step=9986048 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1372
TRAIN_METRICS step=9990144 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1372
TRAIN_METRICS step=9994240 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=1372
TRAIN_METRICS step=9998336 avg_return=97.0500 avg_intrinsic=0.000000 eval_return=0.0000 nonzero_rate=0.0000 sps=1337
Final evaluation:
TEST_METRICS eval_return=0.0000 auc=0.000000 nonzero_rate=0.0000 best_eval_return=0.0000

### frostbite-v5 (scripts/frostbite.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=260.0000 avg_intrinsic=0.000000 eval_return=nan nonzero_rate=nan sps=2096
TRAIN_METRICS step=9986048