I’m building a custom environment using the OpenAI Gym framework and training agents with keras-rl library. My main challenge is that the available actions change based on the current state.
In my environment, there are 46 total possible actions, but depending on the current state, only a subset of these actions are valid. For instance, in one particular state, only 7 actions might be allowed.
I searched through the Gym documentation but couldn’t find clear guidance on implementing state-dependent action spaces. There’s an open GitHub issue about this topic, but no official solution yet.
I’m also confused about how the DQN agent from keras-rl selects actions when the action space is restricted. Does it randomly sample from all possible actions or just the valid ones?
Has anyone successfully implemented dynamic action masking in Gym environments? What approaches work best for handling this scenario?
Had this exact problem in a multi-agent sim where agent abilities kept changing at runtime. I didn’t want to mess with the core environment or agent code, so I built a middleware layer that sits between them. The middleware tracks which actions are valid based on current state and handles the translation. When the agent wants to pick an action, it only sees valid Q-values. Then the middleware translates that choice back to what the environment expects. Your DQN thinks it’s dealing with a normal fixed action space, but the environment gets properly filtered actions. For keras-rl, I just overrode the agent’s forward method to hit the middleware first. Kept everything modular without forking any libraries. Works great when your valid actions follow patterns or can be grouped.
Dynamic action spaces suck, but automation beats manual masking or penalties every time.
I had the same problem with a logistics setup where valid routes kept changing with traffic. Skip the hardcoded masks and negative rewards - just automate the whole validation pipeline.
Build workflows that watch your environment and generate valid actions on the fly. They catch action requests, check them against current rules, then execute or redirect automatically.
For DQN, automate the Q-value filtering. Create workflows that inject valid action masks into your model’s forward pass without messing with keras-rl. Your agent only sees valid choices - no preprocessing headaches.
This scales way better. When I added new actions or changed rules, everything adapted without touching code. Plus you get automatic logging for debugging.
Move validation out of your training loop into automated pipelines that handle state monitoring and action filtering behind the scenes.
just override action_space.sample() in your custom env to return valid actions for the current state. way simpler than masking. your DQN explores properly without wasting time on impossible moves. works great for my chess variant where legal moves change every turn.
Had this exact issue six months back while building a trading environment where actions got locked out based on portfolio state. Gym doesn’t handle this natively, but there’s a solid workaround. I went with action masking at the agent level instead of messing with the environment’s action space. Kept all 46 actions fixed but added a method that returns a binary mask showing which actions are valid right now. Then I tweaked my DQN’s action selection to apply this mask before picking moves. For keras-rl - the standard DQN just samples from everything without checking if actions are valid. You’ll need to jump in during action selection and apply your mask to the Q-values. Usually means setting invalid actions to negative infinity before softmax or epsilon-greedy kicks in. The trick is handling this agent-side rather than changing the environment. Keeps everything compatible with existing RL libraries while getting the dynamic behavior you’re after.
yeah, had the same issue with a card game environment. I skipped dynamic spaces and just slammed the agent with -1000 reward for invalid moves. makes it learn valid actions through trial and error. pretty hacky but works great with standard DQN - no need to modify keras-rl at all.
Had this exact problem building a resource allocation environment. The trick that worked for me: wrap your base environment to handle masking behind the scenes. The wrapper keeps all 46 actions but catches invalid ones in the step function before they go through. I just map invalid actions to a “wait” action or throw an error state. For DQN, I tweaked the policy to mask Q-values during forward passes - zero out invalid actions in the output layer before argmax runs. This keeps everything Gym-compliant and solves the dynamic action headache. You don’t need to mess with the action space definition, just control what can actually execute. Plugs right into keras-rl once you hook the action selection pipeline correctly.