Agent Conversation: claude-opus-4.6

Model: claude-opus-4.6

Total steps: 7

Messages: 16

UserStep 0

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": "# =====================================================================\nclass IntrinsicBonusModule(nn.Module):\n    \"\"\"Dual-Signal Intrinsic Motivation: RND + Forward Dynamics hybrid.\n\n    Combines state-level novelty (RND prediction error against a frozen random\n    target) with transition-level novelty (action-conditioned forward dynamics\n    prediction error in a learned feature space).  The two signals are\n    complementary: RND excels at long-horizon exploration (PrivateEye) while\n    forward dynamics captures action-dependent surprises (Frostbite).  Both\n    bonuses are summed after shared reward normalization.\n    \"\"\"\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        feature_output = 7 * 7 * 64\n\n        # ---------- RND component ----------\n        self.rnd_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.rnd_target.parameters():\n            param.requires_grad = False\n\n        self.rnd_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\n        # ---------- Forward-dynamics component ----------\n        # Shared encoder for obs/next_obs feature extraction\n        self.fd_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, 256)),\n            nn.ReLU(),\n        )\n        # Inverse model regularises the encoder to capture controllable aspects\n        self.inverse_model = nn.Sequential(\n            layer_init(nn.Linear(512, 256)),\n            nn.ReLU(),\n            layer_init(nn.Linear(256, action_dim), std=0.01),\n        )\n        # Forward model predicts next-state features from (state_feat, action)\n        self.forward_model = nn.Sequential(\n            layer_init(nn.Linear(256 + action_dim, 256)),\n            nn.ReLU(),\n            layer_init(nn.Linear(256, 256)),\n        )\n\n        # Relative weight of forward-dynamics bonus vs RND bonus\n        self.fd_weight = 0.5\n\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(\n                0, envs.single_action_space.n, size=(self.args.num_envs,)\n            )\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 (\n            list(self.rnd_predictor.parameters())\n            + list(self.fd_encoder.parameters())\n            + list(self.inverse_model.parameters())\n            + list(self.forward_model.parameters())\n        )\n\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    # ------------------------------------------------------------------\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        norm_curr = self._normalize_obs(obs)\n\n        # RND bonus: prediction error on next-state features\n        target_feat = self.rnd_target(norm_next)\n        pred_feat = self.rnd_predictor(norm_next)\n        rnd_bonus = (target_feat - pred_feat).pow(2).sum(1) / 2\n\n        # Forward-dynamics bonus: prediction error of next-state latent\n        curr_z = self.fd_encoder(norm_curr)\n        next_z = self.fd_encoder(norm_next)\n        pred_next_z = self.forward_model(\n            torch.cat([curr_z, self._one_hot(actions)], dim=1)\n        )\n        fd_bonus = 0.5 * (pred_next_z - next_z).pow(2).mean(dim=1)\n\n        return (rnd_bonus + self.fd_weight * fd_bonus).detach()\n\n    # ------------------------------------------------------------------\n    def normalize_rollout_rewards(self, rollout_intrinsic: torch.Tensor) -> torch.Tensor:\n        discounted = np.stack(\n            [\n                self.discounted_reward.update(rps)\n                for rps in rollout_intrinsic.cpu().numpy()\n            ],\n            axis=0,\n        )\n        flat = discounted.reshape(-1)\n        self.reward_rms.update_from_moments(\n            float(flat.mean()), float(flat.var()), int(flat.size)\n        )\n        return rollout_intrinsic / float(np.sqrt(self.reward_rms.var + 1e-8))\n\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        norm_curr = self._normalize_obs(batch_obs)\n\n        # RND loss (with subsampling mask, same as reference RND)\n        pred_feat = self.rnd_predictor(norm_next)\n        target_feat = self.rnd_target(norm_next).detach()\n        rnd_per = F.mse_loss(pred_feat, target_feat, reduction=\"none\").mean(-1)\n        mask = (\n            torch.rand(len(rnd_per), device=self.device) < self.args.update_proportion\n        ).float()\n        rnd_loss = (rnd_per * mask).sum() / torch.clamp(mask.sum(), min=1.0)\n\n        # Forward / inverse dynamics loss\n        curr_z = self.fd_encoder(norm_curr)\n        next_z = self.fd_encoder(norm_next)\n        pred_next_z = self.forward_model(\n            torch.cat([curr_z, self._one_hot(batch_actions)], dim=1)\n        )\n        forward_loss = 0.5 * (pred_next_z - next_z.detach()).pow(2).mean()\n        pred_action = self.inverse_model(torch.cat([curr_z, next_z], dim=1))\n        inverse_loss = F.cross_entropy(pred_action, batch_actions.long())\n\n        return rnd_loss + inverse_loss + 0.2 * forward_loss\n\n\ndef mix_advantages(\n    ext_advantages: torch.Tensor,\n    int_advantages: torch.Tensor,\n    args: Args,\n) -> torch.Tensor:\n    return args.ext_coef * ext_advantages + args.int_coef * int_advantages\n\n"
7}

Tool ResultStep 1

OK: Replaced lines 179..219 with 195 line(s) in cleanrl/cleanrl/custom_intrinsic_exploration.py. Editable range: 179–373.

[Current file: cleanrl/cleanrl/custom_intrinsic_exploration.py | editable: 179–373 | total: 646 lines]
   179  # =====================================================================
   180  class IntrinsicBonusModule(nn.Module):
   181      """Dual-Signal Intrinsic Motivation: RND + Forward Dynamics hybrid.
       ... (189 more lines) ...
   371  ) -> torch.Tensor:
   372      return args.ext_coef * ext_advantages + args.int_coef * int_advantages
   373

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=139.9000 avg_intrinsic=0.003319 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9986048 avg_return=139.9000 avg_intrinsic=0.003593 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9990144 avg_return=139.0000 avg_intrinsic=0.003790 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9994240 avg_return=140.9500 avg_intrinsic=0.003254 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9998336 avg_return=140.9500 avg_intrinsic=0.003459 eval_return=104.6000 nonzero_rate=1.0000 sps=1110
Final evaluation:
TEST_METRICS eval_return=104.6000 auc=31.850061 nonzero_rate=1.0000 best_eval_return=105.0000

### frostbite-v5 (scripts/frostbite.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=3377.5000 avg_intrinsic=0.019090 eval_retur

AssistantStep 3edit

Tool Input

1{
2  "content": "# =====================================================================\nclass IntrinsicBonusModule(nn.Module):\n    \"\"\"Dual-Signal Intrinsic Motivation (DSIM-v2): RND + Forward Dynamics hybrid.\n\n    Combines state-level novelty (RND) with transition-level novelty (forward\n    dynamics).  Key improvement over v1: each bonus signal is normalized\n    independently before combining, preventing the forward-dynamics signal\n    (which can be noisy early on) from drowning out the RND signal that drives\n    long-horizon exploration.  The fd_weight is kept modest so RND dominates in\n    hard-exploration games like PrivateEye while the forward component provides\n    an additive boost in games like Frostbite where action-dependent surprises\n    matter.\n    \"\"\"\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        # Separate reward normalization for each signal\n        self.rnd_reward_rms = RunningMeanStd()\n        self.fd_reward_rms = RunningMeanStd()\n        self.rnd_discounted = RewardForwardFilter(args.int_gamma)\n        self.fd_discounted = RewardForwardFilter(args.int_gamma)\n\n        feature_output = 7 * 7 * 64\n\n        # ---------- RND component ----------\n        self.rnd_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.rnd_target.parameters():\n            param.requires_grad = False\n\n        self.rnd_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\n        # ---------- Forward-dynamics component ----------\n        self.fd_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, 256)),\n            nn.ReLU(),\n        )\n        self.inverse_model = nn.Sequential(\n            layer_init(nn.Linear(512, 256)),\n            nn.ReLU(),\n            layer_init(nn.Linear(256, action_dim), std=0.01),\n        )\n        self.forward_model = nn.Sequential(\n            layer_init(nn.Linear(256 + action_dim, 256)),\n            nn.ReLU(),\n            layer_init(nn.Linear(256, 256)),\n        )\n\n        # Relative weight: after each signal is independently normalised,\n        # fd_weight controls how much forward-dynamics contributes.\n        # Kept modest (0.3) so RND dominates in hard-exploration games.\n        self.fd_weight = 0.3\n\n        # Buffers to store per-step raw bonuses for separate normalization\n        self._rnd_rollout = None\n        self._fd_rollout = None\n\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(\n                0, envs.single_action_space.n, size=(self.args.num_envs,)\n            )\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 (\n            list(self.rnd_predictor.parameters())\n            + list(self.fd_encoder.parameters())\n            + list(self.inverse_model.parameters())\n            + list(self.forward_model.parameters())\n        )\n\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    # ------------------------------------------------------------------\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        norm_curr = self._normalize_obs(obs)\n\n        # RND bonus\n        target_feat = self.rnd_target(norm_next)\n        pred_feat = self.rnd_predictor(norm_next)\n        rnd_bonus = (target_feat - pred_feat).pow(2).sum(1) / 2\n\n        # Forward-dynamics bonus\n        curr_z = self.fd_encoder(norm_curr)\n        next_z = self.fd_encoder(norm_next)\n        pred_next_z = self.forward_model(\n            torch.cat([curr_z, self._one_hot(actions)], dim=1)\n        )\n        fd_bonus = 0.5 * (pred_next_z - next_z).pow(2).mean(dim=1)\n\n        # Return sum; we store components for separate normalisation later\n        # The caller stores per-step results into int_rewards; we piggy-back\n        # by returning combined raw bonus and keeping component buffers.\n        # However, the training loop only calls compute_bonus once per step\n        # and then normalize_rollout_rewards at the end of the rollout.\n        # We cache per-step components.\n        if self._rnd_rollout is None:\n            self._rnd_rollout = []\n            self._fd_rollout = []\n        self._rnd_rollout.append(rnd_bonus.detach())\n        self._fd_rollout.append(fd_bonus.detach())\n\n        return (rnd_bonus + fd_bonus).detach()  # raw combined (will be replaced in normalize)\n\n    # ------------------------------------------------------------------\n    def normalize_rollout_rewards(self, rollout_intrinsic: torch.Tensor) -> torch.Tensor:\n        \"\"\"Independently normalise RND and FD bonuses then combine.\"\"\"\n        num_steps = rollout_intrinsic.shape[0]\n        num_envs = rollout_intrinsic.shape[1]\n\n        # Reconstruct per-step tensors from cached components\n        if self._rnd_rollout is not None and len(self._rnd_rollout) == num_steps:\n            rnd_rollout = torch.stack(self._rnd_rollout, dim=0)  # (T, E)\n            fd_rollout = torch.stack(self._fd_rollout, dim=0)\n        else:\n            # Fallback: treat entire rollout_intrinsic as RND-only\n            rnd_rollout = rollout_intrinsic\n            fd_rollout = torch.zeros_like(rollout_intrinsic)\n\n        # Clear cache for next rollout\n        self._rnd_rollout = None\n        self._fd_rollout = None\n\n        # Normalize RND rewards\n        rnd_disc = np.stack(\n            [self.rnd_discounted.update(rps) for rps in rnd_rollout.cpu().numpy()],\n            axis=0,\n        )\n        flat_rnd = rnd_disc.reshape(-1)\n        self.rnd_reward_rms.update_from_moments(\n            float(flat_rnd.mean()), float(flat_rnd.var()), int(flat_rnd.size)\n        )\n        rnd_normalized = rnd_rollout / float(np.sqrt(self.rnd_reward_rms.var + 1e-8))\n\n        # Normalize FD rewards\n        fd_disc = np.stack(\n            [self.fd_discounted.update(rps) for rps in fd_rollout.cpu().numpy()],\n            axis=0,\n        )\n        flat_fd = fd_disc.reshape(-1)\n        self.fd_reward_rms.update_from_moments(\n            float(flat_fd.mean()), float(flat_fd.var()), int(flat_fd.size)\n        )\n        fd_normalized = fd_rollout / float(np.sqrt(self.fd_reward_rms.var + 1e-8))\n\n        # Combine with weighting: RND is primary, FD is supplementary\n        return rnd_normalized + self.fd_weight * fd_normalized\n\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        norm_curr = self._normalize_obs(batch_obs)\n\n        # RND loss (with subsampling mask)\n        pred_feat = self.rnd_predictor(norm_next)\n        target_feat = self.rnd_target(norm_next).detach()\n        rnd_per = F.mse_loss(pred_feat, target_feat, reduction=\"none\").mean(-1)\n        mask = (\n            torch.rand(len(rnd_per), device=self.device) < self.args.update_proportion\n        ).float()\n        rnd_loss = (rnd_per * mask).sum() / torch.clamp(mask.sum(), min=1.0)\n\n        # Forward / inverse dynamics loss\n        curr_z = self.fd_encoder(norm_curr)\n        next_z = self.fd_encoder(norm_next)\n        pred_next_z = self.forward_model(\n            torch.cat([curr_z, self._one_hot(batch_actions)], dim=1)\n        )\n        forward_loss = 0.5 * (pred_next_z - next_z.detach()).pow(2).mean()\n        pred_action = self.inverse_model(torch.cat([curr_z, next_z], dim=1))\n        inverse_loss = F.cross_entropy(pred_action, batch_actions.long())\n\n        return rnd_loss + inverse_loss + 0.2 * forward_loss\n\n\ndef mix_advantages(\n    ext_advantages: torch.Tensor,\n\n... (truncated)",
3  "end_line": 373,
4  "filename": "cleanrl/cleanrl/custom_intrinsic_exploration.py",
5  "op": "replace",
6  "start_line": 179
7}

Tool ResultStep 3

OK: Replaced lines 179..373 with 244 line(s) in cleanrl/cleanrl/custom_intrinsic_exploration.py. Editable range: 179–422.

[Current file: cleanrl/cleanrl/custom_intrinsic_exploration.py | editable: 179–422 | total: 695 lines]
   179  # =====================================================================
   180  class IntrinsicBonusModule(nn.Module):
   181      """Dual-Signal Intrinsic Motivation (DSIM-v2): RND + Forward Dynamics hybrid.
       ... (238 more lines) ...
   420  ) -> torch.Tensor:
   421      return args.ext_coef * ext_advantages + args.int_coef * int_advantages
   422

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=9981952 avg_return=103.7500 avg_intrinsic=0.008161 eval_return=nan nonzero_rate=nan sps=1888
TRAIN_METRICS step=9986048 avg_return=103.8000 avg_intrinsic=0.008111 eval_return=nan nonzero_rate=nan sps=1888
TRAIN_METRICS step=9990144 avg_return=104.2000 avg_intrinsic=0.008478 eval_return=nan nonzero_rate=nan sps=1889
TRAIN_METRICS step=9994240 avg_return=104.0500 avg_intrinsic=0.008832 eval_return=nan nonzero_rate=nan sps=1889
TRAIN_METRICS step=9998336 avg_return=103.8000 avg_intrinsic=0.008943 eval_return=107.6000 nonzero_rate=1.0000 sps=1882
Final evaluation:
TEST_METRICS eval_return=107.6000 auc=97.740142 nonzero_rate=1.0000 best_eval_return=109.0000

### frostbite-v5 (scripts/frostbite.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=288.5000 avg_intrinsic=0.020536 eval_return=

AssistantStep 5edit

Tool Input

1{
2  "content": "# =====================================================================\nclass IntrinsicBonusModule(nn.Module):\n    \"\"\"Dual-Signal Intrinsic Motivation: RND + Forward Dynamics hybrid.\n\n    Combines state-level novelty (RND) with transition-level novelty (forward\n    dynamics).  RND drives long-horizon exploration (PrivateEye) while forward\n    dynamics boosts action-dependent surprise (Frostbite/Tutankham).\n    Single-stream reward normalization keeps the implementation simple and\n    stable.  The FD component uses a modest weight so it enhances but does not\n    overwhelm the RND signal.\n    \"\"\"\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        feature_output = 7 * 7 * 64\n\n        # ---------- RND component ----------\n        self.rnd_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.rnd_target.parameters():\n            param.requires_grad = False\n\n        self.rnd_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\n        # ---------- Forward-dynamics component ----------\n        self.fd_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, 256)),\n            nn.ReLU(),\n        )\n        self.inverse_model = nn.Sequential(\n            layer_init(nn.Linear(512, 256)),\n            nn.ReLU(),\n            layer_init(nn.Linear(256, action_dim), std=0.01),\n        )\n        self.forward_model = nn.Sequential(\n            layer_init(nn.Linear(256 + action_dim, 256)),\n            nn.ReLU(),\n            layer_init(nn.Linear(256, 256)),\n        )\n\n        # FD bonus weight relative to RND (kept small to avoid overwhelming RND)\n        self.fd_weight = 0.3\n\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(\n                0, envs.single_action_space.n, size=(self.args.num_envs,)\n            )\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 (\n            list(self.rnd_predictor.parameters())\n            + list(self.fd_encoder.parameters())\n            + list(self.inverse_model.parameters())\n            + list(self.forward_model.parameters())\n        )\n\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    # ------------------------------------------------------------------\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        norm_curr = self._normalize_obs(obs)\n\n        # RND bonus: prediction error on next-state features\n        target_feat = self.rnd_target(norm_next)\n        pred_feat = self.rnd_predictor(norm_next)\n        rnd_bonus = (target_feat - pred_feat).pow(2).sum(1) / 2\n\n        # Forward-dynamics bonus: prediction error of next-state latent\n        curr_z = self.fd_encoder(norm_curr)\n        next_z = self.fd_encoder(norm_next)\n        pred_next_z = self.forward_model(\n            torch.cat([curr_z, self._one_hot(actions)], dim=1)\n        )\n        fd_bonus = 0.5 * (pred_next_z - next_z).pow(2).mean(dim=1)\n\n        # Combine with weighting; single-stream normalization handles scale\n        return (rnd_bonus + self.fd_weight * fd_bonus).detach()\n\n    # ------------------------------------------------------------------\n    def normalize_rollout_rewards(self, rollout_intrinsic: torch.Tensor) -> torch.Tensor:\n        discounted = np.stack(\n            [\n                self.discounted_reward.update(rps)\n                for rps in rollout_intrinsic.cpu().numpy()\n            ],\n            axis=0,\n        )\n        flat = discounted.reshape(-1)\n        self.reward_rms.update_from_moments(\n            float(flat.mean()), float(flat.var()), int(flat.size)\n        )\n        return rollout_intrinsic / float(np.sqrt(self.reward_rms.var + 1e-8))\n\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        norm_curr = self._normalize_obs(batch_obs)\n\n        # RND loss (with subsampling mask)\n        pred_feat = self.rnd_predictor(norm_next)\n        target_feat = self.rnd_target(norm_next).detach()\n        rnd_per = F.mse_loss(pred_feat, target_feat, reduction=\"none\").mean(-1)\n        mask = (\n            torch.rand(len(rnd_per), device=self.device) < self.args.update_proportion\n        ).float()\n        rnd_loss = (rnd_per * mask).sum() / torch.clamp(mask.sum(), min=1.0)\n\n        # Forward / inverse dynamics loss\n        curr_z = self.fd_encoder(norm_curr)\n        next_z = self.fd_encoder(norm_next)\n        pred_next_z = self.forward_model(\n            torch.cat([curr_z, self._one_hot(batch_actions)], dim=1)\n        )\n        forward_loss = 0.5 * (pred_next_z - next_z.detach()).pow(2).mean()\n        pred_action = self.inverse_model(torch.cat([curr_z, next_z], dim=1))\n        inverse_loss = F.cross_entropy(pred_action, batch_actions.long())\n\n        return rnd_loss + inverse_loss + 0.2 * forward_loss\n\n\ndef mix_advantages(\n    ext_advantages: torch.Tensor,\n    int_advantages: torch.Tensor,\n    args: Args,\n) -> torch.Tensor:\n    return args.ext_coef * ext_advantages + args.int_coef * int_advantages\n\n",
3  "end_line": 422,
4  "filename": "cleanrl/cleanrl/custom_intrinsic_exploration.py",
5  "op": "replace",
6  "start_line": 179
7}

Tool ResultStep 5

OK: Replaced lines 179..422 with 193 line(s) in cleanrl/cleanrl/custom_intrinsic_exploration.py. Editable range: 179–371.

[Current file: cleanrl/cleanrl/custom_intrinsic_exploration.py | editable: 179–371 | total: 644 lines]
   179  # =====================================================================
   180  class IntrinsicBonusModule(nn.Module):
   181      """Dual-Signal Intrinsic Motivation: RND + Forward Dynamics hybrid.
       ... (187 more lines) ...
   369  ) -> torch.Tensor:
   370      return args.ext_coef * ext_advantages + args.int_coef * int_advantages
   371

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=165.7000 avg_intrinsic=0.001882 eval_return=nan nonzero_rate=nan sps=1032
TRAIN_METRICS step=9986048 avg_return=165.7000 avg_intrinsic=0.002117 eval_return=nan nonzero_rate=nan sps=1032
TRAIN_METRICS step=9990144 avg_return=165.7000 avg_intrinsic=0.002471 eval_return=nan nonzero_rate=nan sps=1032
TRAIN_METRICS step=9994240 avg_return=165.9000 avg_intrinsic=0.002082 eval_return=nan nonzero_rate=nan sps=1032
TRAIN_METRICS step=9998336 avg_return=165.9500 avg_intrinsic=0.002078 eval_return=35.8000 nonzero_rate=1.0000 sps=1009
Final evaluation:
TEST_METRICS eval_return=35.8000 auc=43.127573 nonzero_rate=1.0000 best_eval_return=104.4000

### frostbite-v5 (scripts/frost

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=139.9000 avg_intrinsic=0.003319 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9986048 avg_return=139.9000 avg_intrinsic=0.003593 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9990144 avg_return=139.0000 avg_intrinsic=0.003790 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9994240 avg_return=140.9500 avg_intrinsic=0.003254 eval_return=nan nonzero_rate=nan sps=1136
TRAIN_METRICS step=9998336 avg_return=140.9500 avg_intrinsic=0.003459 eval_return=104.6000 nonzero_rate=1.0000 sps=1110
Final evaluation:
TEST_METRICS eval_return=104.6000 auc=31.850061 nonzero_rate=1.0000 best_eval_return=105.0000

### frostbite-v5 (scripts/frostbite.sh)
Training metrics (last steps):
TRAIN_METRICS step=9981952 avg_return=3377.5000 avg_intrinsic=0.019090 eval_return=nan nonzero_rate=nan sps=1430
TRAIN_METRICS