fixing the reward updates

[m2kulkarni]

No description provided.

[greptile-apps]

Greptile Overview

Greptile Summary

Implemented per-agent reward conditioning by replacing hardcoded reward values with dynamically sampled weight arrays. The changes enable agents to learn from varied reward functions during training.

Key changes:

• Replaced static reward_vehicle_collision, reward_offroad_collision, and reward_goal values with per-agent collision_weights[i], offroad_weights[i], and goal_weights[i] arrays
• Fixed post-respawn goal reward scaling by multiplying reward_goal_post_respawn constant by goal_weights[i]
• Corrected configuration bounds: swapped offroad_weight_lb (-0.4) and offroad_weight_ub (0.0), and swapped discount_weight_lb (0.80) and discount_weight_ub (0.98)
• Increased num_agents from 512 to 1024 for larger batch training
• Extended SLURM array to test all 5 conditioning types (0-4 vs 1-2)

Confidence Score: 5/5

• This PR is safe to merge with minimal risk
• All changes are internally consistent and mathematically correct. The weight array replacements maintain the same reward computation logic while enabling per-agent conditioning. Configuration bounds were properly corrected to ensure lb < ub. No breaking changes or edge cases detected.
• No files require special attention

Important Files Changed

File Analysis

Filename	Score	Overview
pufferlib/config/ocean/drive.ini	5/5	Updated conditioning weight bounds and increased num_agents from 512 to 1024; swapped lb/ub values correctly for offroad and discount weights
pufferlib/ocean/drive/drive.h	5/5	Replaced hardcoded reward values with per-agent weight arrays for reward conditioning; correctly scaled post-respawn goal rewards
scripts/run.sh	5/5	Expanded SLURM array from 1-2 to 0-4 to test all five conditioning types (none, reward, entropy, discount, all)

Sequence Diagram

sequenceDiagram
    participant Config as drive.ini
    participant Reset as c_reset()
    participant Agent as Agent Loop
    participant Step as c_step()
    participant Rewards as Reward Calculation

    Config->>Reset: Load conditioning bounds (lb/ub)
    Reset->>Reset: Sample collision_weights[i]
    Reset->>Reset: Sample offroad_weights[i]
    Reset->>Reset: Sample goal_weights[i]
    Reset->>Agent: Initialize agents with weights
    
    Agent->>Step: Execute agent action
    Step->>Rewards: Check collision state
    alt Vehicle Collision
        Rewards->>Rewards: reward = collision_weights[i]
    else Offroad
        Rewards->>Rewards: reward = offroad_weights[i]
    end
    
    Step->>Rewards: Check goal distance
    alt Goal Reached (Post Respawn)
        Rewards->>Rewards: reward += reward_goal_post_respawn * goal_weights[i]
    else Goal Reached (Generate New)
        Rewards->>Rewards: reward += goal_weights[i]
    else Goal Reached (Stop)
        Rewards->>Rewards: reward = goal_weights[i]
    end
    
    Rewards->>Agent: Return per-agent rewards

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[greptile-apps]

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[charliemolony]

could place the policy in the co player policies folder?